Dawei Yin | Papers With Code

<!doctype html> <html lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> <script> const GTAG_ENABLED = true ; const GTAG_TRACKING_ID = "UA-121182717-1"; const SENTRY_DSN_FRONTEND = "".trim(); const GLOBAL_CSRF_TOKEN = 'M8rClqVcFEgGCDDJXNbfzT4LLG9IrDsWafFysoE85bnvALe7WrxByoAalv8ByxfB'; const MEDIA_URL = "https://production-media.paperswithcode.com/"; const ASSETS_URL = "https://production-assets.paperswithcode.com"; run_after_frontend_loaded = window.run_after_frontend_loaded || []; </script> <link rel="preconnect" href="https://production-assets.paperswithcode.com"><link rel="dns-prefetch" href="https://production-assets.paperswithcode.com"><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/65e877e527022735c1a1.woff2" crossorigin><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/917632e36982ca7933c8.woff2" crossorigin><link rel="preload" as="font" type="font/woff2" href="https://production-assets.paperswithcode.com/perf/fonts/f1405bd8a987c2ea8a67.woff2" crossorigin><script>(()=>{if(GTAG_ENABLED){const t=document.createElement("script");function n(){window.dataLayer.push(arguments)}t.src=`https://www.googletagmanager.com/gtag/js?id=${GTAG_TRACKING_ID}`,document.head.appendChild(t),window.dataLayer=window.dataLayer||[],window.gtag=n,n("js",new Date),n("config",GTAG_TRACKING_ID),window.captureOutboundLink=function(t){n("event","click",{event_category:"outbound",event_label:t})}}else window.captureOutboundLink=function(n){document.location=n}})();</script><style>:root{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #21cbce;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-primary: #0d6efd;--bs-secondary: #6c757d;--bs-success: #198754;--bs-info: #21cbce;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #f8f9fa;--bs-dark: #212529;--bs-font-sans-serif: system-ui, -apple-system, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", "Liberation Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";--bs-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;--bs-gradient: linear-gradient(180deg, rgba(255, 255, 255, 0.15), rgba(255, 255, 255, 0))}@font-face{font-family:"Lato";font-style:normal;font-weight:300;font-display:swap;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/917632e36982ca7933c8.woff2) format("woff2")}@font-face{font-family:"Lato";font-style:normal;font-weight:400;font-display:swap;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/65e877e527022735c1a1.woff2) format("woff2")}@font-face{font-family:"Lato";font-style:normal;font-weight:700;font-display:swap;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/f1405bd8a987c2ea8a67.woff2) format("woff2")}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/b63de31899ec71cfb870.woff) format("woff");font-display:swap;font-weight:normal;font-style:normal}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/5d5c7512cb539fb279b2.woff) format("woff");font-display:swap;font-weight:bold;font-style:normal}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/357ce3503c6299bc1b58.woff) format("woff");font-display:swap;font-weight:normal;font-style:italic}@font-face{font-family:"Computer Modern Serif";src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/c83e6f15d4c7568ee872.woff) format("woff");font-display:swap;font-weight:bold;font-style:italic}@font-face{font-family:"Exo";font-style:normal;font-weight:100;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/729c812ee9989426abb1.woff2) format("woff2");font-display:swap}@font-face{font-family:"Nunito";font-style:normal;font-weight:400;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/39a18f443d434999b89b.woff2) format("woff2");font-display:swap}@font-face{font-family:"Nunito";font-style:normal;font-weight:700;src:local(""),url(https://production-assets.paperswithcode.com/perf/fonts/4ad349571e28bb59c5a5.woff2) format("woff2");font-display:swap}*,*::before,*::after{box-sizing:border-box}@media(prefers-reduced-motion: no-preference){:root{scroll-behavior:smooth}}body{margin:0;font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans","Liberation Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;background-color:#fff;-webkit-text-size-adjust:100%;-webkit-tap-highlight-color:rgba(0,0,0,0)}[tabindex="-1"]:focus:not(:focus-visible){outline:0 !important}hr{margin:1rem 0;color:#000;background-color:currentColor;border:0;opacity:.1}hr:not([size]){height:1px}h6,h5,h4,h3,h2,h1{margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}h1{font-size:calc(1.375rem + 1.5vw)}@media(min-width: 1200px){h1{font-size:2.5rem}}h2{font-size:calc(1.325rem + 0.9vw)}@media(min-width: 1200px){h2{font-size:2rem}}h3{font-size:calc(1.3rem + 0.6vw)}@media(min-width: 1200px){h3{font-size:1.75rem}}h4{font-size:calc(1.275rem + 0.3vw)}@media(min-width: 1200px){h4{font-size:1.5rem}}h5{font-size:1.25rem}h6{font-size:1rem}p{margin-top:0;margin-bottom:1rem}ol,ul{padding-left:2rem}ol,ul{margin-top:0;margin-bottom:1rem}ul ul{margin-bottom:0}b,strong{font-weight:bolder}small{font-size:0.875em}a{color:#0d6efd;text-decoration:none}a:hover{color:#0a58ca;text-decoration:none}a:not([href]):not([class]),a:not([href]):not([class]):hover{color:inherit;text-decoration:none}pre,code{font-family:var(--bs-font-monospace);font-size:1em;direction:ltr /* rtl:ignore */;unicode-bidi:bidi-override}pre{display:block;margin-top:0;margin-bottom:1rem;overflow:auto;font-size:0.875em}code{font-size:0.875em;color:#d63384;word-wrap:break-word}a>code{color:inherit}figure{margin:0 0 1rem}img,svg{vertical-align:middle}table{caption-side:bottom;border-collapse:collapse}th{text-align:inherit;text-align:-webkit-match-parent}thead,tbody,tfoot,tr,td,th{border-color:inherit;border-style:solid;border-width:0}label{display:inline-block}button{border-radius:0}button:focus:not(:focus-visible){outline:0}input,button,select,optgroup,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,select{text-transform:none}[role=button]{cursor:pointer}select{word-wrap:normal}[list]::-webkit-calendar-picker-indicator{display:none}button,[type=button],[type=reset],[type=submit]{-webkit-appearance:button}button:not(:disabled),[type=button]:not(:disabled),[type=reset]:not(:disabled),[type=submit]:not(:disabled){cursor:pointer}::-moz-focus-inner{padding:0;border-style:none}textarea{resize:vertical}::-webkit-datetime-edit-fields-wrapper,::-webkit-datetime-edit-text,::-webkit-datetime-edit-minute,::-webkit-datetime-edit-hour-field,::-webkit-datetime-edit-day-field,::-webkit-datetime-edit-month-field,::-webkit-datetime-edit-year-field{padding:0}::-webkit-inner-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:textfield}::-webkit-search-decoration{-webkit-appearance:none}::-webkit-color-swatch-wrapper{padding:0}::file-selector-button{font:inherit}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}iframe{border:0}[hidden]{display:none !important}.list-unstyled{padding-left:0;list-style:none}small,.small{font-size:0.875em;font-weight:400}.footer{display:block;margin-top:30px;padding:15px;border-top:1px solid #e0e0e0;font-size:13px;color:#aaa;text-align:center}.footer a{color:#999}.footer-contact{margin-bottom:5px}.footer-contact-item{display:inline-block}.footer-links>*:not(:last-child){margin-right:1rem}.icon-wrapper{display:inline-block;width:1em;height:1em;contain:strict;fill:currentcolor;box-sizing:content-box !important}.icon-wrapper.icon-fa{position:relative;top:2px}.icon-wrapper svg{display:block;height:100%;width:100%}.icon-wrapper[data-name=slack] svg{width:200%;height:200%;transform:translate(-25%, -25%)}.icon-wrapper:not(.icon-color)>svg>*{stroke:currentColor}.navbar-brand .icon-wrapper{color:#21cbce;width:30px;height:30px;vertical-align:middle}.navbar-mobile-twitter{margin-right:18px !important;padding-top:1px}.navbar-mobile-twitter a{color:#1d9bf0}.navbar-mobile-twitter .icon-wrapper{width:23px;height:23px}.header-search{margin-bottom:26px}.header-search form{position:relative}.header-search .icon{color:gray;position:absolute !important;top:50% !important;left:initial !important;padding-right:0 !important;transform:translateY(-50%);right:22px;padding:0;height:20px;width:20px}.header-search .icon .icon-wrapper{width:100%;height:100%;top:0}.nav-link-social-icon{color:#1d9bf0;width:25px;height:25px}.nav-link-social-icon-slack{vertical-align:middle}@media(min-width: 992px){.header-search{margin:0}.header-search .icon{right:10px}.nav-link-social-icon{width:20px;height:20px}.nav-link-social-icon-slack{width:22px;height:22px}} </style><link href="https://production-assets.paperswithcode.com/static/css/13.a0e289cc.chunk.css" rel="stylesheet"><link href="https://production-assets.paperswithcode.com/static/css/main.cd7ec85b.chunk.css" rel="stylesheet">  <title>Dawei Yin | Papers With Code</title> <meta name="description" content="Papers by Dawei Yin with links to code and results." />  <meta property="og:title" content="Papers with Code - Dawei Yin"> <meta property="og:description" content="Papers by Dawei Yin with links to code and results."> <meta property="og:image" content="https://paperswithcode.com/static/index.jpeg"> <meta property="og:url" content="https://paperswithcode.com/search?q=author%3ADawei+Yin">  <meta name="twitter:card" content="summary_large_image"> <meta name="twitter:site" content="@paperswithcode"> <meta name="twitter:title" content="Papers with Code - Dawei Yin"> <meta name="twitter:description" content="Papers by Dawei Yin with links to code and results."> <meta name="twitter:creator" content="@paperswithcode"> <meta name="twitter:url" content="https://paperswithcode.com/search?q=author%3ADawei+Yin"> <meta name="twitter:domain" content="paperswithcode.com">  <script type="application/ld+json">{ "@context": "http://schema.org", "@graph": { "@type": "WebPage", "name": "Dawei Yin", "description": "Papers by Dawei Yin with links to code and results.", "url": "https://paperswithcode.com/search?q=author%3ADawei+Yin", "image": "https://paperswithcode.com/static/index.jpeg", "headline": "Dawei Yin" } }</script> <meta name="theme-color" content="#fff"/> <link rel="manifest" href="https://production-assets.paperswithcode.com/static/manifest.web.json"> </head> <body> <nav class="navbar navbar-expand-lg navbar-light header"> <a class="navbar-brand" href="/"> <span class=" icon-wrapper" data-name="pwc"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path d="M88 128h48v256H88zm144 0h48v256h-48zm-72 16h48v224h-48zm144 0h48v224h-48zm72-16h48v256h-48z"/><path d="M104 104V56H16v400h88v-48H64V104zm304-48v48h40v304h-40v48h88V56z"/></svg></span> </a> <div class="navbar-mobile-twitter d-lg-none"> <a rel="noreferrer" href="https://twitter.com/paperswithcode"> <span class=" icon-wrapper icon-fa icon-fa-brands" data-name="twitter"><svg viewBox="0 0 512.001 515.25" xmlns="http://www.w3.org/2000/svg"><path d="M459.37 152.016c.326 4.548.326 9.097.326 13.645 0 138.72-105.583 298.558-298.559 298.558C101.685 464.22 46.457 447 0 417.114c8.447.973 16.568 1.298 25.34 1.298 49.054 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.113-72.772 6.499.975 12.996 1.624 19.819 1.624 9.42 0 18.843-1.3 27.613-3.573-48.08-9.747-84.142-51.98-84.142-102.984v-1.3c13.968 7.798 30.213 12.67 47.43 13.32-28.263-18.843-46.78-51.006-46.78-87.391 0-19.492 5.196-37.36 14.294-52.954 51.654 63.674 129.3 105.258 216.364 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.827 46.782-104.934 104.934-104.934 30.214 0 57.502 12.67 76.671 33.136 23.715-4.548 46.455-13.319 66.599-25.34-7.798 24.367-24.366 44.834-46.132 57.828 21.117-2.274 41.584-8.122 60.426-16.244-14.292 20.791-32.161 39.309-52.628 54.253z"/></svg></span> </a> </div> <button class="navbar-toggler" type="button" data-toggle="collapse" data-bs-toggle="collapse" data-target="#top-menu" data-bs-target="#top-menu" aria-controls="top-menu" aria-expanded="false" aria-label="Toggle navigation" > <span class="navbar-toggler-icon"></span> </button> <div class="collapse navbar-collapse" id="top-menu"> <ul class="navbar-nav mr-auto navbar-nav__left light-header"> <li class="nav-item header-search"> <form action="/search" method="get" id="id_global_search_form" autocomplete="off"> <input type="text" name="q_meta" style="display:none" id="q_meta" /> <input type="hidden" name="q_type" id="q_type" /> <input id="id_global_search_input" autocomplete="off" value="" name='q' class="global-search" type="search" placeholder='Search'/> <button type="submit" class="icon"><span class=" icon-wrapper icon-fa icon-fa-light" data-name="search"><svg viewBox="0 0 512.025 520.146" xmlns="http://www.w3.org/2000/svg"><path d="M508.5 482.6c4.7 4.7 4.7 12.3 0 17l-9.9 9.9c-4.7 4.7-12.3 4.7-17 0l-129-129c-2.2-2.3-3.5-5.3-3.5-8.5v-10.2C312 396 262.5 417 208 417 93.1 417 0 323.9 0 209S93.1 1 208 1s208 93.1 208 208c0 54.5-21 104-55.3 141.1H371c3.2 0 6.2 1.2 8.5 3.5zM208 385c97.3 0 176-78.7 176-176S305.3 33 208 33 32 111.7 32 209s78.7 176 176 176z"/></svg></span></button> </form> </li> <li class="nav-item"> <a class="nav-link" href="/sota"> Browse State-of-the-Art </a> </li> <li class="nav-item"> <a class="nav-link" href="/datasets"> Datasets </a> </li> <li class="nav-item"> <a class="nav-link" href="/methods">Methods</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" role="button" id="navbarDropdownRepro" data-toggle="dropdown" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false" > More </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownRepro"> <a class="dropdown-item" href="/newsletter">Newsletter</a> <a class="dropdown-item" href="/rc2022">RC2022</a> <div class="dropdown-divider"></div> <a class="dropdown-item" href="/about">About</a> <a class="dropdown-item" href="/trends">Trends</a> <a class="dropdown-item" href="https://portal.paperswithcode.com/"> Portals </a> <a class="dropdown-item" href="/libraries"> Libraries </a> </div> </li> </ul> <ul class="navbar-nav ml-auto navbar-nav__right navbar-subscribe justify-content-center align-items-center"> <li class="nav-item"> <a class="nav-link" rel="noreferrer" href="https://twitter.com/paperswithcode"> <span class="nav-link-social-icon icon-wrapper icon-fa icon-fa-brands" data-name="twitter"><svg viewBox="0 0 512.001 515.25" xmlns="http://www.w3.org/2000/svg"><path d="M459.37 152.016c.326 4.548.326 9.097.326 13.645 0 138.72-105.583 298.558-298.559 298.558C101.685 464.22 46.457 447 0 417.114c8.447.973 16.568 1.298 25.34 1.298 49.054 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.113-72.772 6.499.975 12.996 1.624 19.819 1.624 9.42 0 18.843-1.3 27.613-3.573-48.08-9.747-84.142-51.98-84.142-102.984v-1.3c13.968 7.798 30.213 12.67 47.43 13.32-28.263-18.843-46.78-51.006-46.78-87.391 0-19.492 5.196-37.36 14.294-52.954 51.654 63.674 129.3 105.258 216.364 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.827 46.782-104.934 104.934-104.934 30.214 0 57.502 12.67 76.671 33.136 23.715-4.548 46.455-13.319 66.599-25.34-7.798 24.367-24.366 44.834-46.132 57.828 21.117-2.274 41.584-8.122 60.426-16.244-14.292 20.791-32.161 39.309-52.628 54.253z"/></svg></span> </a> </li> <li class="nav-item"> <a id="signin-link" class="nav-link" href="/accounts/login?next=/search">Sign In</a> </li> </ul> </div> </nav>  <div class="modal fade" id="emailModal" tabindex="-1" role="dialog" aria-labelledby="emailModalLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h3 class="modal-title" id="emailModalLabel">Subscribe to the PwC Newsletter</h3> <button type="button" class="close" data-dismiss="modal" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <form action="" method="post"> <div class="modal-body"> <div class="modal-body-info-text"> Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets.<br/><br/> <a href="/newsletter">Read previous issues</a> </div> <input type="hidden" name="csrfmiddlewaretoken" value="M8rClqVcFEgGCDDJXNbfzT4LLG9IrDsWafFysoE85bnvALe7WrxByoAalv8ByxfB"> <input placeholder="Enter your email" type="email" class="form-control pwc-email" name="address" id="id_address" max_length="100" required> </div> <div class="modal-footer"> <button type="submit" class="btn btn-primary">Subscribe</button> </div> </form> </div> </div> </div>  <div class="modal fade" id="loginModal" tabindex="-1" role="dialog" aria-labelledby="loginModalLabel" aria-hidden="true"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <h5 class="modal-title" id="loginModalLabel">Join the community</h5> <button type="button" class="close btn-close" data-dismiss="modal" data-bs-dismiss="modal" aria-label="Close"> <span aria-hidden="true">×</span> </button> </div> <div class="login-modal-message"> You need to <a href="/accounts/login?next=/search">log in</a> to edit.<br/> You can <a href="/accounts/register?next=/search">create a new account</a> if you don't have one.<br/><br/> </div> </div> </div> </div> <div class="container content content-buffer "> <div class="author-search-page"> <div class="title home-page-header"> <div class="row"> <div class="col-lg-6"> <h2 class="home-page-title"> Search Results for author: <span class="author-name">Dawei Yin</span> </h2> <h3 class="home-page-subtitle">Found <b>129</b> papers, <b>55</b> papers with code</h3> </div> <div class="col-lg-6 index-group"> <div id="authorSortBy" style="float: right;" class="btn-group pull-right search-page-order-by" role="group"> <div class="btn-group dropdown" role="group"> <button id="btnGroupDrop1" type="button" class="btn btn-outline-secondary dropdown-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Date Published <span class=" icon-wrapper icon-fa icon-fa-regular" data-name="chevron-down"><svg viewBox="0 0 448 513.795" xmlns="http://www.w3.org/2000/svg"><path d="M441.9 168.28c4.7 4.7 4.7 12.3 0 17l-209.4 209.4c-4.7 4.7-12.3 4.7-17 0L6.1 185.28c-4.7-4.7-4.7-12.3 0-17l19.8-19.8c4.7-4.7 12.3-4.7 17 0L224 329.18l181.1-180.7c4.701-4.7 12.302-4.7 17 0z"/></svg></span> </button> <div class="dropdown-menu" aria-labelledby="btnGroupDrop1"> <a class="dropdown-item" href="?q=author%3ADawei+Yin&order_by=date">Date Published</a> <a class="dropdown-item" href="?q=author%3ADawei+Yin&order_by=stars">Github Stars</a> </div> </div> <a href="?q=author%3ADawei+Yin&order=asc" type="button" class="btn btn-outline-secondary"> <span class=" icon-wrapper icon-fa icon-fa-regular" data-name="arrow-down"><svg viewBox="0 0 448 513.795" xmlns="http://www.w3.org/2000/svg"><path d="M441.9 251.08c4.7 4.7 4.7 12.3 0 17l-209.4 209.4c-4.7 4.7-12.3 4.7-17 0L6.1 268.08c-4.7-4.7-4.7-12.3 0-17l19.8-19.8c4.7-4.7 12.3-4.7 17 0L198 386.38V44.98c0-6.599 5.401-12 12-12h28c6.6 0 12 5.401 12 12v341.4l155.1-155.1c4.701-4.7 12.302-4.7 17 0z"/></svg></span> </a> </div> </div> </div> </div> <div class="infinite-container text-center"> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/original-content-is-all-you-need-an-empirical"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1091051.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/original-content-is-all-you-need-an-empirical">Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/original-content-is-all-you-need-an-empirical#code">no code implementations</a> • <span class="item-conference-link"> <a href="/conference/coling-2022-10"> COLING 2022 </a> </span> • <span class="author-span "> <a href="/author/liang-wen">Liang Wen</a></span>, <span class="author-span "> <a href="/author/juan-li">Juan Li</a></span>, <span class="author-span "> <a href="/author/houfeng-wang">Houfeng Wang</a></span>, <span class="author-span "> <a href="/author/yingwei-luo">Yingwei Luo</a></span>, <span class="author-span "> <a href="/author/xiaolin-wang">Xiaolin Wang</a></span>, <span class="author-span "> <a href="/author/xiaodong-zhang">Xiaodong Zhang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances.</p> <div class="sota"> </div> <p> <a href="/task/answer-selection"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Answer Selection</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/original-content-is-all-you-need-an-empirical" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/original-content-is-all-you-need-an-empirical#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/cross-model-control-improving-multiple-large"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/cross-model-control-improving-multiple-large">Cross-model Control: Improving Multiple Large Language Models in One-time Training</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/cross-model-control-improving-multiple-large#code">1 code implementation</a> • <span class="author-name-text item-date-pub">23 Oct 2024</span> • <span class="author-span "> <a href="/author/jiayi-wu">Jiayi Wu</a></span>, <span class="author-span "> <a href="/author/hao-sun">Hao Sun</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/xiang-li">Xiang Li</a></span>, <span class="author-span "> <a href="/author/ming-gao">Ming Gao</a></span> </p> <p class="item-strip-abstract">Based on this insight, we incorporate a tiny language model with a minimal number of parameters.</p> <div class="sota"> </div> <p> <a href="/task/instruction-following"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Instruction Following</span> </span> </a> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 10</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/cross-model-control-improving-multiple-large" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/cross-model-control-improving-multiple-large#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/adaswitch-adaptive-switching-between-small"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/adaswitch-adaptive-switching-between-small">AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/adaswitch-adaptive-switching-between-small#code">no code implementations</a> • <span class="author-name-text item-date-pub">17 Oct 2024</span> • <span class="author-span "> <a href="/author/hao-sun">Hao Sun</a></span>, <span class="author-span "> <a href="/author/jiayi-wu">Jiayi Wu</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/yue-feng">Yue Feng</a></span>, <span class="author-span "> <a href="/author/bo-wang-1">Bo wang</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/yan-zhang">Yan Zhang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Recent advancements in large language models (LLMs) have been remarkable.</p> <div class="sota"> </div> <p> <a href="/task/mathematical-reasoning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Mathematical Reasoning</span> </span> </a> <a href="/task/question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/56ae901a-265f-415f-b175-ce54133d648b.jpg"> <span>Question Answering</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/adaswitch-adaptive-switching-between-small" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/adaswitch-adaptive-switching-between-small#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/mair-a-massive-benchmark-for-evaluating"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/0f523f89-6223-409c-bdc0-42fd201acca0.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/mair-a-massive-benchmark-for-evaluating">MAIR: A Massive Benchmark for Evaluating Instructed Retrieval</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/mair-a-massive-benchmark-for-evaluating#code">1 code implementation</a> • <span class="author-name-text item-date-pub">14 Oct 2024</span> • <span class="author-span "> <a href="/author/weiwei-sun">Weiwei Sun</a></span>, <span class="author-span "> <a href="/author/zhengliang-shi">Zhengliang Shi</a></span>, <span class="author-span "> <a href="/author/jiulong-wu">Jiulong Wu</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/xinyu-ma">Xinyu Ma</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/min-cao">Min Cao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instructions.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/re-ranking"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Re-Ranking</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/mair-a-massive-benchmark-for-evaluating#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 14</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/mair-a-massive-benchmark-for-evaluating" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/mair-a-massive-benchmark-for-evaluating#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/jailjudge-a-comprehensive-jailbreak-judge"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/9de1ab0a-354d-470d-bf79-2f70b51f8e9e.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/jailjudge-a-comprehensive-jailbreak-judge">JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/jailjudge-a-comprehensive-jailbreak-judge#code">1 code implementation</a> • <span class="author-name-text item-date-pub">11 Oct 2024</span> • <span class="author-span "> <a href="/author/fan-liu">Fan Liu</a></span>, <span class="author-span "> <a href="/author/yue-feng">Yue Feng</a></span>, <span class="author-span "> <a href="/author/zhao-xu">Zhao Xu</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/xinyu-ma">Xinyu Ma</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/hao-liu-1">Hao liu</a></span> </p> <p class="item-strip-abstract">Despite advancements in enhancing LLM safety against jailbreak attacks, evaluating LLM defenses remains a challenge, with current methods often lacking explainability and generalization to complex scenarios, leading to incomplete assessments (e. g., direct judgment without reasoning, low F1 score of GPT-4 in complex cases, bias in multilingual scenarios).</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 26</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/jailjudge-a-comprehensive-jailbreak-judge" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/jailjudge-a-comprehensive-jailbreak-judge#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/macpo-weak-to-strong-alignment-via-multi"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/macpo-weak-to-strong-alignment-via-multi">MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/macpo-weak-to-strong-alignment-via-multi#code">no code implementations</a> • <span class="author-name-text item-date-pub">10 Oct 2024</span> • <span class="author-span "> <a href="/author/yougang-lyu">Yougang Lyu</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/zihan-wang">Zihan Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/pengjie-ren">Pengjie Ren</a></span>, <span class="author-span "> <a href="/author/maarten-de-rijke">Maarten de Rijke</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">As large language models (LLMs) are rapidly advancing and achieving near-human capabilities, aligning them with human values is becoming more urgent.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/macpo-weak-to-strong-alignment-via-multi" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/macpo-weak-to-strong-alignment-via-multi#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/from-exploration-to-mastery-enabling-llms-to"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/from-exploration-to-mastery-enabling-llms-to">From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/from-exploration-to-mastery-enabling-llms-to#code">1 code implementation</a> • <span class="author-name-text item-date-pub">10 Oct 2024</span> • <span class="author-span "> <a href="/author/changle-qu">Changle Qu</a></span>, <span class="author-span "> <a href="/author/sunhao-dai">Sunhao Dai</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jun-xu">Jun Xu</a></span>, <span class="author-span "> <a href="/author/ji-rong-wen">Ji-Rong Wen</a></span> </p> <p class="item-strip-abstract">Tool learning enables Large Language Models (LLMs) to interact with external environments by invoking tools, serving as an effective strategy to mitigate the limitations inherent in their pre-training data.</p> <div class="sota"> </div> <p> <a href="/task/diversity"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Diversity</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 4</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/from-exploration-to-mastery-enabling-llms-to" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/from-exploration-to-mastery-enabling-llms-to#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/fltlm-an-intergrated-long-context-large"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/fltlm-an-intergrated-long-context-large">FltLM: An Intergrated Long-Context Large Language Model for Effective Context Filtering and Understanding</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/fltlm-an-intergrated-long-context-large#code">no code implementations</a> • <span class="author-name-text item-date-pub">9 Oct 2024</span> • <span class="author-span "> <a href="/author/jingyang-deng">Jingyang Deng</a></span>, <span class="author-span "> <a href="/author/zhengyang-shen">Zhengyang Shen</a></span>, <span class="author-span "> <a href="/author/boyang-wang">Boyang Wang</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/ying-nie">Ying Nie</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jinwen-ma">Jinwen Ma</a></span> </p> <p class="item-strip-abstract">The development of Long-Context Large Language Models (LLMs) has markedly advanced natural language processing by facilitating the process of textual data across long documents and multiple corpora.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a href="/task/large-language-model"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Large Language Model</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/fltlm-an-intergrated-long-context-large#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/fltlm-an-intergrated-long-context-large" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/fltlm-an-intergrated-long-context-large#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/information-discovery-in-e-commerce"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/information-discovery-in-e-commerce">Information Discovery in e-Commerce</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/information-discovery-in-e-commerce#code">no code implementations</a> • <span class="author-name-text item-date-pub">8 Oct 2024</span> • <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span "> <a href="/author/xiangnan-he">Xiangnan He</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/maarten-de-rijke">Maarten de Rijke</a></span> </p> <p class="item-strip-abstract">Methods for information discovery in e-commerce largely focus on improving the effectiveness of e-commerce search and recommender systems, on enriching and using knowledge graphs to support e-commerce, and on developing innovative question answering and bot-based solutions that help to connect people to goods and services.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/knowledge-graphs"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Graphs</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/information-discovery-in-e-commerce#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/information-discovery-in-e-commerce" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/information-discovery-in-e-commerce#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/pre-trained-graphformer-based-ranking-at-web"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/pre-trained-graphformer-based-ranking-at-web">Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/pre-trained-graphformer-based-ranking-at-web#code">no code implementations</a> • <span class="author-name-text item-date-pub">25 Sep 2024</span> • <span class="author-span "> <a href="/author/yuchen-li">Yuchen Li</a></span>, <span class="author-span "> <a href="/author/haoyi-xiong">Haoyi Xiong</a></span>, <span class="author-span "> <a href="/author/linghe-kong">Linghe Kong</a></span>, <span class="author-span "> <a href="/author/zeyi-sun">Zeyi Sun</a></span>, <span class="author-span "> <a href="/author/hongyang-chen">Hongyang Chen</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Both Transformer and Graph Neural Networks (GNNs) have been employed in the domain of learning to rank (LTR).</p> <div class="sota"> </div> <p> <a href="/task/learning-to-rank"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Learning-To-Rank</span> </span> </a> <a href="/task/link-prediction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000031-326cd034.jpg"> <span>Link Prediction</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/pre-trained-graphformer-based-ranking-at-web#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/pre-trained-graphformer-based-ranking-at-web" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/pre-trained-graphformer-based-ranking-at-web#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/generative-pre-trained-ranking-model-with"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/generative-pre-trained-ranking-model-with">Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/generative-pre-trained-ranking-model-with#code">no code implementations</a> • <span class="author-name-text item-date-pub">25 Sep 2024</span> • <span class="author-span "> <a href="/author/yuchen-li">Yuchen Li</a></span>, <span class="author-span "> <a href="/author/haoyi-xiong">Haoyi Xiong</a></span>, <span class="author-span "> <a href="/author/linghe-kong">Linghe Kong</a></span>, <span class="author-span "> <a href="/author/jiang-bian">Jiang Bian</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/guihai-chen">Guihai Chen</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Learning to rank (LTR) is widely employed in web searches to prioritize pertinent webpages from retrieved content based on input queries.</p> <div class="sota"> </div> <p> <a href="/task/learning-to-rank"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Learning-To-Rank</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/generative-pre-trained-ranking-model-with" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/generative-pre-trained-ranking-model-with#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/llms-persona-plug-personalized-llms"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/llms-persona-plug-personalized-llms">LLMs + Persona-Plug = Personalized LLMs</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/llms-persona-plug-personalized-llms#code">no code implementations</a> • <span class="author-name-text item-date-pub">18 Sep 2024</span> • <span class="author-span "> <a href="/author/jiongnan-liu">Jiongnan Liu</a></span>, <span class="author-span "> <a href="/author/yutao-zhu">Yutao Zhu</a></span>, <span class="author-span "> <a href="/author/shuting-wang">Shuting Wang</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/erxue-min">Erxue Min</a></span>, <span class="author-span "> <a href="/author/yu-lu">Yu Lu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/zhicheng-dou">Zhicheng Dou</a></span> </p> <p class="item-strip-abstract">By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their own parameters.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/llms-persona-plug-personalized-llms" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/llms-persona-plug-personalized-llms#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/gencrf-generative-clustering-and"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAoKCgoKCgsMDAsPEA4QDxYUExMUFiIYGhgaGCIzICUgICUgMy03LCksNy1RQDg4QFFeT0pPXnFlZXGPiI+7u/v/wgALCACcAPIBAREA/8QAGgABAAMBAQEAAAAAAAAAAAAAAAECAwQFB//aAAgBAQAAAAD7MzwvSN7iZFOeUa6BYIACQgASHP5ukY6RefVkMfLFs7q9vcGPlWvvtEW6JDHx4tEWW6duwMuCnVvdC0yMfMrMXrMOr0DPQK8/RYBh460VSjv9Ew3cfNE1tEdfYGHjwiQ7fTMdnJ5ts9cbW6/RDHx9J00hMdPSYbuHzwT1+kGHlVshC3X6Jlq4OBW0J6/TDHyEpRDt9A5ulw+fIpPd6IYeTNaXgju9I5+hzZkyrtuGHjXotEJ7vRMds+N0JsrG1hh5GsRInp9Apfl8nSkaRER6PeMPHibUmYd3ohycFF6TbLXt7Rl49kxFkdfoByeXE2gPQ7hj5kDTVHR1By+Ulbalo7esZePVJEz2eiHNwTRpES7eoYeNMm0zXr7QQASCAkAAAAAA/8QARBAAAQIEBAMDCQYCCAcAAAAAAQIRAAMSIQQiMUETUWEgMnEUQlJTgZGSodIQI2JyscEzggUVMEOywtHhJVBjg6Li8P/aAAgBAQABPwD7JsxEpNS3Z9hAxshW6vamFY6QBSCpmcmk3jy3Du1RdvRMeWyPxb7QifKmJfiAD8RAMcSX61HxCOJL9aj4hHEl+tR8QjiS/Wo+IQlSVXBB8OytaZaSsuw5XMDGyDur4THlmHBFSlN0EDG4Z0grVf8ACY8tkA+d7ujxLnypossJu2a36xxJfrUfEI4kv1qPiEcSX61HxCOJL9aj4hCVoVooFuRB7bQ0MIaGENDQ0N22hoaGhoaGhobt4pKjKSUpWpQWCySBcDrBkTuIU8HEFIQKSJkCStCFAyMSSTS3EHi4e4jgTV/3E8fdqDmYNnMS5U8S01YfEA0s3FG8GXNKlPhp7gG9Y3uYRJmAk+T4q3OcCCDAkrRUgycQb+s6C14lCdJWFeS4hwNFTX1iWoqQklNJa437WIJEhZqIYbf7EQVTEqvMWvnmJPzMCYqnvzXI0BVcDdnhJWy0ibNuARmLc2N4K10AJVMaom5O1ucKWpOi16g6n3C8GYqkATJhdIUTUf8AWAtZDmYsBRYgEi7WJvFazetYZ9CQ55i+ggLXQ9czVjc7XBGbpeMGqYVLClL0diS3uJPaxC1ysPMmpF0pJDgm/gI/rOeXHDRYA9xfytH9Y4kG0lB0d0riVjcTNWEhEpibuFJYjqee0FeNSWrwju2qof8ApIKZYw4TyBLiP+IpAfyZwDuqHx/o4d3NnVD/ANIecnDMlmuq/Ptzx9xMgIUSrJdSg3QcrogBbVUlh063bJApNR4YJoZ2azajJCwqsIEogPZmb/BAdRYS7MNRy3aiAkpJFD1ADTYl7mmEpdYGjHMKf/SFoqUAlDOCdAO9sTSbQcJNopCUhyTqDrt3YwsmZJWpUwAkjof0A7U8VSljZoTIFKqjt01282BJQGdStDsD/lhpRDDCyc7+b/tAQ1+BLChcEc/GAuaRdCQwtfeHnAAGUBfZUBU0A5Eu/PURVOBfhoZvSiqfSAZaQOioS7XDHs4h+BMpd+j/ALQFYipSq5j1EtmaxgzJ6iDXNA/mEVYgZeLMcHV1N1hUydmzzRcDzm5XhM2ezErIALd57wlWJ0rVozur3nqI404pAK5hsdXcPAXPA/iTDcEOVN1fpzjiT1IU0+a4OgKmbk3XaMEpajMqUs6M7sx8ewpZC0JYZnv2lEJSSdADAxkj0lb+araEqC0gh/7HEgnDzbPaACDdDAj0X/ywAaVEAkOLFIZjcuKYTSSoLTqb5dhv3YZnZPeHogOeRywpx5jdKdnd3pgXD8N7uMov7aYZyAUX3yjU6EZYSbPQXceaNGZjkhgKshII9EC4Gtk7RgaUTJlIbKGdIHzAHYmBPFlO7uw+3GTZklMpSDS5IuAQfiIjyycxzp0GyPq32jyueQghY3csnQFnOaPLcQBaYnfZLBjzq3g4ue7CaguAQQE8nI70HGTtRMTSSbCm3JzVHlU/O01BbonX4oGMnN30gnQsn6ow09cxS0LLlIHIfIE9rFMJExzYC52+ZECkJJtrrlZjoO9vAoc0KRoXLp+qGsHYpb8IcbF6oKdVOl30NPi9lQwcl0aVG6d+ZqiuWQTUlrFnT4t3oK0VIUogP1T9UAJrOZJb8rtyOYQVpJLs2pNSfec0YFwuYOmzbnoT2FpPElnl1+3GAkSmPnOYZRSbqYEWYwmu916cjpAE0AKClM+jK8XhKZgUkVZb830eEhdR7zvfKq29oNYSKTMuepNrgRnIIzBm5xgCqpddVwOfuv2p5AkTCz87tBnKyjhKuLffubmEzJJCCAus1Onim3K7bwFyyShpgDH+931iVikSyQlBU4vVMew8YONQkkcNOo86DjkDWSjR+8x0sIOPSADwUFiXFUeXJJJEpDc6oTjk+pR8Vud4w+JE5JAQlJ5gu47E5IM2UerdT9uOAaUpQBYksQP3BikLBLAgBzYaD+WAUJqJSl0EAaAC9/NgSwQ6ikWdIZJ6N3YCEM+VmLlh4ADLBSjLQ1RA2S9v5YNBALJABdwA7kad2DQVnKDUXGn0xgQApdLMwGwvzLAX7WI/gTS7MIrCSkVMFdSzfFCiR3Cq4uXP1wlVNSnLgMG32Nq4JCqVIW6SATc2ItfNFVyxNLDe/Mh64Qq9lE2F3JAHUFcBgSKncWAVt1zwTQs0qcbXP1xUXJQTpZy4Jf8ANGBupTLfLe5+pXYXTUhwdbfbjikCU5bMWNtvEiEmSUqU6fykpfqRmMApWoqWpABFjlNvi2gKTW6KEtoTT7hmgkOFClgAxBToLP3rw8oVBJSU+KSTyHeitIAKVIcuGdL2/mjKCFGit+afq2jAlNS6WYgOA37E9rEXkTNTbaGXSbKPiFQEqEtIZT39ODWbivXep290KQqsd9ir8UfeubHR/OZjztAExKu6dCfOOsULJCVAsx9IfNoomFQSkHT8XtctBrKRlUGJvnFjtaMI4mTHfu9dSeoHYmPxpXRzq32492lBKtyDc/sRCipqRU4Ja5frvAKl00k3e9SmbbeApVBcKIW7ZiSCC/OAVl1Mpw1gSA24Z4SCFJJfTYq92sJMzM9TXu6oeYALKuPSPsa/vjAllzE3YJDFyR89+1iQ+Hm+EBCQFOhLAg2A3tsmKQKXS4f0R+yYEpgykC4CgaRpo/djLQAEpITfuh3NzamGTnYOLWpAB/8AGGSAKqXYgApH0wJYXYAEhJLsNtfNgJS4pCdC2UajbuxQhLg0/CPpjAUoUqlrJ2ABHuA7E0gTpIa/24qRMmpllIFlbtf3vAwMtkitT1Pon/SPIZQdpqz1pTbwtBwEtOk1TX2T8rQMDKRUAslwdUps5flBwaCqqshmsEpa3sg4GVRQJqtX0SfZpHkMpg61Fgdkt+kSZCcO9BOjdrEgnDzOohwprjvDcOwDenBpzmtJYgBlA7vuoawKarFg97gsDv34eqkunW5qBDnfvxlD5gq17jc7OuAZa1K00bUe+yoKgzq97j64fapKrhr/ADGeHpW+uYt4gOWzbbxgjdTEHKD1f4ldiZSZso31YfZMXLQkKXMpFQ9p5QuZIU5GPWASSACGD7B9hClyWvj1jxIdoTicMkGqcDSA79dCTBxWGFuOh+Tx5ThnI4qXFm3eBisIUvx0k9DHlWG0M5IGpg4nDil5ycwcHZo8qwrPx0MG35wiZLWMiwbAn26dnEmnDzVUuw0qp35xxZYYmUpNrffJvFeHPDFKiqlVSeJoRdoE2Tmsq/8A1RYQlclxZTOX+9EBcmwCVEjfijeEqkrWkUqur1oLEwJkpZahVyA3FHhBXLZJSkn/ALoF4E2TsFNofvRvGBWhSpgTsH79XYXKUtcpaQLK15Dp9mMLSX0zD3+wiBMprZdyDurm+lWkJmyyUlSrgAakfuYTNSE1BZqL8/qgTE0jPZR5qsxv50GclVQqLFtzzf0oM1NKVPoOov8AFCZwpRUvUHcmzsbFUCZa6wlNYa523JqgTUJ881NuSR4gVRgS6ZmaoOObc9yeziLSJhJZh+/QiEzaTaYk1CxrNiDuCreOLRMAE3QjckN8UKmKa0zQ2zKcj4orBBHE5XdXzIVHGJDVl9NVfVCZpQjvVOB5x9zFUFaQtbLVswqJv8UJnJNIqOYWuoC5/NHECVBlr7ujk33PejCF5s3M7dSf1J7WMfg2d6hcP+wMCtySP8XuAaE1oqF9WqdRitQU4QXc+kRDTSQdAWsaoVxGDVAPap39whllKmqLE2ze0aQagpikgsQ92gVM33tifNUzn3wtJzXU9JbXV9hGDJKVkvc7uwO7A9md/Ama6bPz6RQqnKVi93CopUaznYFwCFOx5W1EC6Ug197XNoTvaAVBSXqZ9KVaO1zCEqBAIL30qIZnNzA4h0SagTso9eUFCwVJShbubmqGnJAFKyRoWURbq0JQthlWHd1Mva9i1jGCCwZhJmMQLF2J53btY5uAHSGKhqx/V4QwS4Sip2DAC50L0wlIpUEoTUw2Gu75YSxCaqT/ACi3hlhgKsqanLJ263pgukJypPsAZ7sGTCQkqJpFz3up1bLCSWvTY8hY/DCkprC2DML9fCmFBTOyWYt0cuwyxgmoWKnzPpz9g7M8pEiY6QrmGe0Fctv4EuncUX5WvFaEkEYeWwGyNzqNYUZaWPk0km+iLatzipAAeRKY7BHXxh0BlDDShfZG2+8JWJc0FMmSDoFBNx1d4GMnsFAJ5EU38dYVjZ4SCEpYbUm/zgYyaQkMH/L+t4w8+ZOKgsJAADMGL+89rHCiQKtah7PmIUoNUVgXA2DsPzRfYakdf0XAWSgFw1Vm2O7mqEswLPbRwLbHvQhKpgKpSSWIdm+qBJmgh5KwQD0v8UHD4hiOHM6KIt8lQJGINVcte75WLt0VaPJppPcWLlzZmO/etGFRMlhYUCASGGn7nsz0k4eYWgoTsDcFstgfhhQVSzZnG23wQgppFSdzsbvsRRtBQzdW832sMsFICj0u9L9LimGSbeaw835HLttFIYkCm92HsFqIUAUWAcFrp2ezZYIBWSALAKulnL/ljAUpK2QAGFv/AJKXHaxa+HKCn84Ws3zhWIWCKlpLEOcjGFYgs6VpsTsnePKFKy1jk2RyYOJLJNYFiPMhGJmFQZYFrjKSwudI8tm6ibQCXDlNgdhBxeILATyHc1Oi40eDjJ2qZoFNjdELxU4FuKxYAd2MJOM5MxSluxDANy5p7OJKfJprnboPmY+7LVKCRbdIc6iF2KGUgAh3NNhyEKUkE5k3A9GKkoPeSXAOqYSkzCEICSQNHTdi0HDT1XMq9ncJctaPJcSpLcKk7d0uXYuIGGxA1kh7MclgIGDxANpJKf5RbxjCypktRrQ2UDbbw/s97AdkBu0w+1hyjcMN7/8AKP/Z');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/gencrf-generative-clustering-and">GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/gencrf-generative-clustering-and#code">no code implementations</a> • <span class="author-name-text item-date-pub">17 Sep 2024</span> • <span class="author-span "> <a href="/author/wonduk-seo">Wonduk Seo</a></span>, <span class="author-span "> <a href="/author/haojie-zhang">Haojie Zhang</a></span>, <span class="author-span "> <a href="/author/yueyang-zhang">Yueyang Zhang</a></span>, <span class="author-span "> <a href="/author/changhao-zhang">Changhao Zhang</a></span>, <span class="author-span "> <a href="/author/songyao-duan">Songyao Duan</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/daiting-shi">Daiting Shi</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Query reformulation is a well-known problem in Information Retrieval (IR) aimed at enhancing single search successful completion rate by automatically modifying user's input query.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/gencrf-generative-clustering-and" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/gencrf-generative-clustering-and#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/opencity-open-spatio-temporal-foundation"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/933f9bde-8816-4c9b-b1a9-ec103f449028.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/opencity-open-spatio-temporal-foundation">OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/opencity-open-spatio-temporal-foundation#code">1 code implementation</a> • <span class="author-name-text item-date-pub">16 Aug 2024</span> • <span class="author-span "> <a href="/author/zhonghang-li">Zhonghang Li</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/lei-shi">Lei Shi</a></span>, <span class="author-span "> <a href="/author/yong-xu">Yong Xu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences.</p> <div class="sota"> </div> <p> <a href="/task/traffic-prediction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000582-a51d2d0f.jpg"> <span>Traffic Prediction</span> </span> </a> <a href="/task/zero-shot-generalization"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Zero-shot Generalization</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 80</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/opencity-open-spatio-temporal-foundation" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/opencity-open-spatio-temporal-foundation#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/darec-a-disentangled-alignment-framework-for"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/darec-a-disentangled-alignment-framework-for">DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/darec-a-disentangled-alignment-framework-for#code">no code implementations</a> • <span class="author-name-text item-date-pub">15 Aug 2024</span> • <span class="author-span "> <a href="/author/xihong-yang">Xihong Yang</a></span>, <span class="author-span "> <a href="/author/heming-jing">Heming Jing</a></span>, <span class="author-span "> <a href="/author/zixing-zhang">Zixing Zhang</a></span>, <span class="author-span "> <a href="/author/jindong-wang">Jindong Wang</a></span>, <span class="author-span "> <a href="/author/huakang-niu">Huakang Niu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/yu-lu">Yu Lu</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/xinwang-liu">Xinwang Liu</a></span>, <span class="author-span "> <a href="/author/en-zhu">En Zhu</a></span>, <span class="author-span "> <a href="/author/defu-lian">Defu Lian</a></span>, <span class="author-span "> <a href="/author/erxue-min">Erxue Min</a></span> </p> <p class="item-strip-abstract">In this work, we prove that directly aligning the representations of LLMs and collaborative models is sub-optimal for enhancing downstream recommendation tasks performance, based on the information theorem.</p> <div class="sota"> </div> <p> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/darec-a-disentangled-alignment-framework-for#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/darec-a-disentangled-alignment-framework-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/darec-a-disentangled-alignment-framework-for#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/powerful-and-flexible-personalized-text-to"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/powerful-and-flexible-personalized-text-to">Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/powerful-and-flexible-personalized-text-to#code">1 code implementation</a> • <span class="author-name-text item-date-pub">9 Jul 2024</span> • <span class="author-span "> <a href="/author/fanyue-wei">Fanyue Wei</a></span>, <span class="author-span "> <a href="/author/wei-zeng">Wei Zeng</a></span>, <span class="author-span "> <a href="/author/zhenyang-li">Zhenyang Li</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/lixin-duan">Lixin Duan</a></span>, <span class="author-span "> <a href="/author/wen-li">Wen Li</a></span> </p> <p class="item-strip-abstract">Personalized text-to-image models allow users to generate varied styles of images (specified with a sentence) for an object (specified with a set of reference images).</p> <div class="sota"> </div> <p> <a href="/task/sentence"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Sentence</span> </span> </a> <a href="/task/text-to-image-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/47fa71ad-77b3-4e24-933f-8a6e00fc3767.jpg"> <span>Text-to-Image Generation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 36</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/powerful-and-flexible-personalized-text-to" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/powerful-and-flexible-personalized-text-to#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/inversecoder-unleashing-the-power-of"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/7e4b152f-1602-4d59-b68c-e505d6e13ad4.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/inversecoder-unleashing-the-power-of">InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/inversecoder-unleashing-the-power-of#code">1 code implementation</a> • <span class="author-name-text item-date-pub">8 Jul 2024</span> • <span class="author-span "> <a href="/author/yutong-wu">Yutong Wu</a></span>, <span class="author-span "> <a href="/author/di-huang">Di Huang</a></span>, <span class="author-span "> <a href="/author/wenxuan-shi">Wenxuan Shi</a></span>, <span class="author-span "> <a href="/author/wei-wang">Wei Wang</a></span>, <span class="author-span "> <a href="/author/lingzhe-gao">Lingzhe Gao</a></span>, <span class="author-span "> <a href="/author/shihao-liu">Shihao Liu</a></span>, <span class="author-span "> <a href="/author/ziyuan-nan">Ziyuan Nan</a></span>, <span class="author-span "> <a href="/author/kaizhao-yuan">Kaizhao Yuan</a></span>, <span class="author-span "> <a href="/author/rui-zhang">Rui Zhang</a></span>, <span class="author-span "> <a href="/author/xishan-zhang">Xishan Zhang</a></span>, <span class="author-span "> <a href="/author/zidong-du">Zidong Du</a></span>, <span class="author-span "> <a href="/author/qi-guo">Qi Guo</a></span>, <span class="author-span "> <a href="/author/yewen-pu">Yewen Pu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/xing-hu">Xing Hu</a></span>, <span class="author-span "> <a href="/author/yunji-chen">Yunji Chen</a></span> </p> <p class="item-strip-abstract">Recent advancements in open-source code large language models (LLMs) have demonstrated remarkable coding abilities by fine-tuning on the data generated from powerful closed-source LLMs such as GPT-3. 5 and GPT-4 for instruction tuning.</p> <div class="sota"> </div> <p> <a href="/task/code-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/658fcf6a-52ca-4865-b111-28bf61c346dc.jpg"> <span>Code Generation</span> </span> </a> <a href="/task/code-summarization-1"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Code Summarization</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/inversecoder-unleashing-the-power-of#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 9</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/inversecoder-unleashing-the-power-of" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/inversecoder-unleashing-the-power-of#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/when-search-engine-services-meet-large"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/when-search-engine-services-meet-large">When Search Engine Services meet Large Language Models: Visions and Challenges</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/when-search-engine-services-meet-large#code">no code implementations</a> • <span class="author-name-text item-date-pub">28 Jun 2024</span> • <span class="author-span "> <a href="/author/haoyi-xiong">Haoyi Xiong</a></span>, <span class="author-span "> <a href="/author/jiang-bian">Jiang Bian</a></span>, <span class="author-span "> <a href="/author/yuchen-li">Yuchen Li</a></span>, <span class="author-span "> <a href="/author/xuhong-li-1">Xuhong LI</a></span>, <span class="author-span "> <a href="/author/mengnan-du">Mengnan Du</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/sumi-helal">Sumi Helal</a></span> </p> <p class="item-strip-abstract">Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we search for and retrieve information, understand content, and interact with internet services.</p> <div class="sota"> </div> <p> <a href="/task/learning-to-rank"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Learning-To-Rank</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/when-search-engine-services-meet-large" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/when-search-engine-services-meet-large#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/hyperbolic-knowledge-transfer-in-cross-domain"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2406.17289.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/hyperbolic-knowledge-transfer-in-cross-domain">Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/hyperbolic-knowledge-transfer-in-cross-domain#code">no code implementations</a> • <span class="author-name-text item-date-pub">25 Jun 2024</span> • <span class="author-span "> <a href="/author/xin-yang">Xin Yang</a></span>, <span class="author-span "> <a href="/author/heng-chang">Heng Chang</a></span>, <span class="author-span "> <a href="/author/zhijian-lai">Zhijian Lai</a></span>, <span class="author-span "> <a href="/author/jinze-yang">Jinze Yang</a></span>, <span class="author-span "> <a href="/author/xingrun-li">Xingrun Li</a></span>, <span class="author-span "> <a href="/author/yu-lu">Yu Lu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/erxue-min">Erxue Min</a></span> </p> <p class="item-strip-abstract">Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been gaining more attention in recent years.</p> <div class="sota"> </div> <p> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/hyperbolic-knowledge-transfer-in-cross-domain#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/hyperbolic-knowledge-transfer-in-cross-domain" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/hyperbolic-knowledge-transfer-in-cross-domain#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/tourrank-utilizing-large-language-models-for"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/tourrank-utilizing-large-language-models-for">TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/tourrank-utilizing-large-language-models-for#code">1 code implementation</a> • <span class="author-name-text item-date-pub">17 Jun 2024</span> • <span class="author-span "> <a href="/author/yiqun-chen">Yiqun Chen</a></span>, <span class="author-span "> <a href="/author/qi-liu">Qi Liu</a></span>, <span class="author-span "> <a href="/author/yi-zhang">Yi Zhang</a></span>, <span class="author-span "> <a href="/author/weiwei-sun">Weiwei Sun</a></span>, <span class="author-span "> <a href="/author/daiting-shi">Daiting Shi</a></span>, <span class="author-span "> <a href="/author/jiaxin-mao">Jiaxin Mao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">However, several significant challenges still persist in LLMs for ranking: (1) LLMs are constrained by limited input length, precluding them from processing a large number of documents simultaneously; (2) The output document sequence is influenced by the input order of documents, resulting in inconsistent ranking outcomes; (3) Achieving a balance between cost and ranking performance is quite challenging.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 4</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/tourrank-utilizing-large-language-models-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/tourrank-utilizing-large-language-models-for#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/the-fall-of-rome-understanding-the-collapse"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/4843e0d4-9d0e-43a0-9b73-1393f01f1b35.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/the-fall-of-rome-understanding-the-collapse">Understanding the Collapse of LLMs in Model Editing</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/the-fall-of-rome-understanding-the-collapse#code">1 code implementation</a> • <span class="author-name-text item-date-pub">17 Jun 2024</span> • <span class="author-span "> <a href="/author/wanli-yang">Wanli Yang</a></span>, <span class="author-span "> <a href="/author/fei-sun">Fei Sun</a></span>, <span class="author-span "> <a href="/author/jiajun-tan">Jiajun Tan</a></span>, <span class="author-span "> <a href="/author/xinyu-ma">Xinyu Ma</a></span>, <span class="author-span "> <a href="/author/du-su">Du Su</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/huawei-shen">HuaWei Shen</a></span> </p> <p class="item-strip-abstract">Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse.</p> <div class="sota"> </div> <p> <a href="/task/model-editing"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Model Editing</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 9</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/the-fall-of-rome-understanding-the-collapse" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/the-fall-of-rome-understanding-the-collapse#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/tool-learning-with-large-language-models-a"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/deb6aca9-331e-4d5e-b962-6888262364e5.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/tool-learning-with-large-language-models-a">Tool Learning with Large Language Models: A Survey</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/tool-learning-with-large-language-models-a#code">1 code implementation</a> • <span class="author-name-text item-date-pub">28 May 2024</span> • <span class="author-span "> <a href="/author/changle-qu">Changle Qu</a></span>, <span class="author-span "> <a href="/author/sunhao-dai">Sunhao Dai</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jun-xu">Jun Xu</a></span>, <span class="author-span "> <a href="/author/ji-rong-wen">Ji-Rong Wen</a></span> </p> <p class="item-strip-abstract">In this survey, we focus on reviewing existing literature from the two primary aspects (1) why tool learning is beneficial and (2) how tool learning is implemented, enabling a comprehensive understanding of tool learning with LLMs.</p> <div class="sota"> </div> <p> <a href="/task/response-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Response Generation</span> </span> </a> <a href="/task/survey"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Survey</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 261</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/tool-learning-with-large-language-models-a" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/tool-learning-with-large-language-models-a#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/atm-adversarial-tuning-multi-agent-system"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/2891a35f-359c-49ff-96f6-7982ac7fd239.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/atm-adversarial-tuning-multi-agent-system">ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/atm-adversarial-tuning-multi-agent-system#code">1 code implementation</a> • <span class="author-name-text item-date-pub">28 May 2024</span> • <span class="author-span "> <a href="/author/junda-zhu">Junda Zhu</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/haibo-shi">Haibo Shi</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/lei-sha">Lei Sha</a></span> </p> <p class="item-strip-abstract">Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) in alleviating hallucinations confronted with knowledge-intensive questions.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/atm-adversarial-tuning-multi-agent-system#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 4</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/atm-adversarial-tuning-multi-agent-system" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/atm-adversarial-tuning-multi-agent-system#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/chain-of-tools-large-language-model-is-an"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/chain-of-tools-large-language-model-is-an">Chain of Tools: Large Language Model is an Automatic Multi-tool Learner</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/chain-of-tools-large-language-model-is-an#code">no code implementations</a> • <span class="author-name-text item-date-pub">26 May 2024</span> • <span class="author-span "> <a href="/author/zhengliang-shi">Zhengliang Shi</a></span>, <span class="author-span "> <a href="/author/shen-gao">Shen Gao</a></span>, <span class="author-span "> <a href="/author/xiuyi-chen">Xiuyi Chen</a></span>, <span class="author-span "> <a href="/author/yue-feng">Yue Feng</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/haibo-shi">Haibo Shi</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/zhumin-chen">Zhumin Chen</a></span>, <span class="author-span "> <a href="/author/suzan-verberne">Suzan Verberne</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, empowering them to solve practical tasks.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a href="/task/large-language-model"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Large Language Model</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/chain-of-tools-large-language-model-is-an" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/chain-of-tools-large-language-model-is-an#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/colt-towards-completeness-oriented-tool"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2405.16089.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/colt-towards-completeness-oriented-tool">Towards Completeness-Oriented Tool Retrieval for Large Language Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/colt-towards-completeness-oriented-tool#code">1 code implementation</a> • <span class="author-name-text item-date-pub">25 May 2024</span> • <span class="author-span "> <a href="/author/changle-qu">Changle Qu</a></span>, <span class="author-span "> <a href="/author/sunhao-dai">Sunhao Dai</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jun-xu">Jun Xu</a></span>, <span class="author-span "> <a href="/author/ji-rong-wen">Ji-Rong Wen</a></span> </p> <p class="item-strip-abstract">Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions, frequently leading to the retrieval of redundant, similar tools.</p> <div class="sota"> <p> <a href="/sota/retrieval-on-toollens"> <img style="height:20px;width:35px;position:relative;top:1px;" src="https://production-media.paperswithcode.com/sota-thumbs/retrieval-on-toollens-small_78e5d47b.png"/> </a>  Ranked #1 on <a class="sota-task" href="/sota/retrieval-on-toollens"> Retrieval on ToolLens </a> </p> </div> <p> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 15</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/colt-towards-completeness-oriented-tool" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/colt-towards-completeness-oriented-tool#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/g3-an-effective-and-adaptive-framework-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2405.14702.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/g3-an-effective-and-adaptive-framework-for">G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/g3-an-effective-and-adaptive-framework-for#code">1 code implementation</a> • <span class="author-name-text item-date-pub">23 May 2024</span> • <span class="author-span "> <a href="/author/pengyue-jia">Pengyue Jia</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/xiaopeng-li">Xiaopeng Li</a></span>, <span class="author-span "> <a href="/author/yuhao-wang">Yuhao Wang</a></span>, <span class="author-span "> <a href="/author/yantong-du">Yantong Du</a></span>, <span class="author-span "> <a href="/author/xiao-han">Xiao Han</a></span>, <span class="author-span "> <a href="/author/xuetao-wei">Xuetao Wei</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span> </p> <p class="item-strip-abstract">Worldwide geolocalization aims to locate the precise location at the coordinate level of photos taken anywhere on the Earth.</p> <div class="sota"> </div> <p> <a href="/task/rag"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/2554a77c-c5ba-475b-bfba-b70a2b41a551.jpg"> <span>RAG</span> </span> </a> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 12</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/g3-an-effective-and-adaptive-framework-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/g3-an-effective-and-adaptive-framework-for#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/a-survey-on-rag-meets-llms-towards-retrieval"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2405.06211.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/a-survey-on-rag-meets-llms-towards-retrieval">A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/a-survey-on-rag-meets-llms-towards-retrieval#code">no code implementations</a> • <span class="author-name-text item-date-pub">10 May 2024</span> • <span class="author-span "> <a href="/author/wenqi-fan">Wenqi Fan</a></span>, <span class="author-span "> <a href="/author/yujuan-ding">Yujuan Ding</a></span>, <span class="author-span "> <a href="/author/liangbo-ning">Liangbo Ning</a></span>, <span class="author-span "> <a href="/author/shijie-wang">Shijie Wang</a></span>, <span class="author-span "> <a href="/author/hengyun-li">Hengyun Li</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/tat-seng-chua-1">Tat-Seng Chua</a></span>, <span class="author-span "> <a href="/author/qing-li">Qing Li</a></span> </p> <p class="item-strip-abstract">Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the generation quality of LLMs.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/rag"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/2554a77c-c5ba-475b-bfba-b70a2b41a551.jpg"> <span>RAG</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/a-survey-on-rag-meets-llms-towards-retrieval#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/a-survey-on-rag-meets-llms-towards-retrieval" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/a-survey-on-rag-meets-llms-towards-retrieval#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/a-survey-of-large-language-models-for-graphs"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/8927b226-76a1-41d6-8b91-086296052fae.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/a-survey-of-large-language-models-for-graphs">A Survey of Large Language Models for Graphs</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/a-survey-of-large-language-models-for-graphs#code">1 code implementation</a> • <span class="author-name-text item-date-pub">10 May 2024</span> • <span class="author-span "> <a href="/author/xubin-ren">Xubin Ren</a></span>, <span class="author-span "> <a href="/author/jiabin-tang">Jiabin Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/nitesh-chawla">Nitesh Chawla</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">This survey aims to serve as a valuable resource for researchers and practitioners eager to leverage large language models in graph learning, and to inspire continued progress in this dynamic field.</p> <div class="sota"> </div> <p> <a href="/task/graph-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Learning</span> </span> </a> <a href="/task/link-prediction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000031-326cd034.jpg"> <span>Link Prediction</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/a-survey-of-large-language-models-for-graphs#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 247</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/a-survey-of-large-language-models-for-graphs" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/a-survey-of-large-language-models-for-graphs#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/govern-gradient-orientation-vote-ensemble-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2405.03764.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/govern-gradient-orientation-vote-ensemble-for">GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/govern-gradient-orientation-vote-ensemble-for#code">no code implementations</a> • <span class="author-name-text item-date-pub">6 May 2024</span> • <span class="author-span "> <a href="/author/wenjie-zhou">Wenjie Zhou</a></span>, <span class="author-span "> <a href="/author/zhenxin-ding">Zhenxin Ding</a></span>, <span class="author-span "> <a href="/author/xiaodong-zhang">Xiaodong Zhang</a></span>, <span class="author-span "> <a href="/author/haibo-shi">Haibo Shi</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance.</p> <div class="sota"> </div> <p> <a href="/task/knowledge-distillation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Distillation</span> </span> </a> <a href="/task/question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/56ae901a-265f-415f-b175-ce54133d648b.jpg"> <span>Question Answering</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/govern-gradient-orientation-vote-ensemble-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/govern-gradient-orientation-vote-ensemble-for#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/the-real-the-better-aligning-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2405.00578.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/the-real-the-better-aligning-large-language">The Real, the Better: Aligning Large Language Models with Online Human Behaviors</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/the-real-the-better-aligning-large-language#code">no code implementations</a> • <span class="author-name-text item-date-pub">1 May 2024</span> • <span class="author-span "> <a href="/author/guanying-jiang">Guanying Jiang</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/haibo-shi">Haibo Shi</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Large language model alignment is widely used and studied to avoid LLM producing unhelpful and harmful responses.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a href="/task/large-language-model"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Large Language Model</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/the-real-the-better-aligning-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/the-real-the-better-aligning-large-language#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/graph-machine-learning-in-the-era-of-large"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2404.14928.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/graph-machine-learning-in-the-era-of-large">Graph Machine Learning in the Era of Large Language Models (LLMs)</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/graph-machine-learning-in-the-era-of-large#code">no code implementations</a> • <span class="author-name-text item-date-pub">23 Apr 2024</span> • <span class="author-span "> <a href="/author/wenqi-fan">Wenqi Fan</a></span>, <span class="author-span "> <a href="/author/shijie-wang">Shijie Wang</a></span>, <span class="author-span "> <a href="/author/jiani-huang">Jiani Huang</a></span>, <span class="author-span "> <a href="/author/zhikai-chen">Zhikai Chen</a></span>, <span class="author-span "> <a href="/author/yu-song">Yu Song</a></span>, <span class="author-span "> <a href="/author/wenzhuo-tang">Wenzhuo Tang</a></span>, <span class="author-span "> <a href="/author/haitao-mao">Haitao Mao</a></span>, <span class="author-span "> <a href="/author/hui-liu">Hui Liu</a></span>, <span class="author-span "> <a href="/author/xiaorui-liu">Xiaorui Liu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/qing-li">Qing Li</a></span> </p> <p class="item-strip-abstract">Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations such as hallucinations and the lack of explainability.</p> <div class="sota"> </div> <p> <a href="/task/few-shot-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Few-Shot Learning</span> </span> </a> <a href="/task/knowledge-graphs"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Graphs</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/graph-machine-learning-in-the-era-of-large#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graph-machine-learning-in-the-era-of-large" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/graph-machine-learning-in-the-era-of-large#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/xl-2-bench-a-benchmark-for-extremely-long"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2404.05446.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/xl-2-bench-a-benchmark-for-extremely-long">XL$^2$Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/xl-2-bench-a-benchmark-for-extremely-long#code">no code implementations</a> • <span class="author-name-text item-date-pub">8 Apr 2024</span> • <span class="author-span "> <a href="/author/xuanfan-ni">Xuanfan Ni</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/piji-li">Piji Li</a></span> </p> <p class="item-strip-abstract">However, prior benchmarks create datasets that ostensibly cater to long-text comprehension by expanding the input of traditional tasks, which falls short to exhibit the unique characteristics of long-text understanding, including long dependency tasks and longer text length compatible with modern LLMs' context window size.</p> <div class="sota"> </div> <p> <a href="/task/long-context-understanding"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Long-Context Understanding</span> </span> </a> <a href="/task/reading-comprehension"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/b1303943-b495-4a43-a95b-dcd7a1f40185.jpg"> <span>Reading Comprehension</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/xl-2-bench-a-benchmark-for-extremely-long" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/xl-2-bench-a-benchmark-for-extremely-long#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/ma4div-multi-agent-reinforcement-learning-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2403.17421.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/ma4div-multi-agent-reinforcement-learning-for">MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/ma4div-multi-agent-reinforcement-learning-for#code">no code implementations</a> • <span class="author-name-text item-date-pub">26 Mar 2024</span> • <span class="author-span "> <a href="/author/yiqun-chen">Yiqun Chen</a></span>, <span class="author-span "> <a href="/author/jiaxin-mao">Jiaxin Mao</a></span>, <span class="author-span "> <a href="/author/yi-zhang">Yi Zhang</a></span>, <span class="author-span "> <a href="/author/dehong-ma">Dehong Ma</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/jun-fan">Jun Fan</a></span>, <span class="author-span "> <a href="/author/daiting-shi">Daiting Shi</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/simiu-gu">Simiu Gu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.</p> <div class="sota"> </div> <p> <a href="/task/diversity"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Diversity</span> </span> </a> <a href="/task/multi-agent-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Multi-agent Reinforcement Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/ma4div-multi-agent-reinforcement-learning-for#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/ma4div-multi-agent-reinforcement-learning-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/ma4div-multi-agent-reinforcement-learning-for#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/improving-the-robustness-of-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2403.14221.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/improving-the-robustness-of-large-language">Improving the Robustness of Large Language Models via Consistency Alignment</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/improving-the-robustness-of-large-language#code">no code implementations</a> • <span class="author-name-text item-date-pub">21 Mar 2024</span> • <span class="author-span "> <a href="/author/yukun-zhao">Yukun Zhao</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/weiwei-sun">Weiwei Sun</a></span>, <span class="author-span "> <a href="/author/guoliang-xing">Guoliang Xing</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/chong-meng">Chong Meng</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">The training process is accomplished by self-rewards inferred from the trained model at the first stage without referring to external human preference resources.</p> <div class="sota"> </div> <p> <a href="/task/diversity"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Diversity</span> </span> </a> <a href="/task/instruction-following"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Instruction Following</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/improving-the-robustness-of-large-language#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/improving-the-robustness-of-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/improving-the-robustness-of-large-language#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/learning-to-use-tools-via-cooperative-and"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2403.03031.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/learning-to-use-tools-via-cooperative-and">Learning to Use Tools via Cooperative and Interactive Agents</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/learning-to-use-tools-via-cooperative-and#code">1 code implementation</a> • <span class="author-name-text item-date-pub">5 Mar 2024</span> • <span class="author-span "> <a href="/author/zhengliang-shi">Zhengliang Shi</a></span>, <span class="author-span "> <a href="/author/shen-gao">Shen Gao</a></span>, <span class="author-span "> <a href="/author/xiuyi-chen">Xiuyi Chen</a></span>, <span class="author-span "> <a href="/author/yue-feng">Yue Feng</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/haibo-shi">Haibo Shi</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/pengjie-ren">Pengjie Ren</a></span>, <span class="author-span "> <a href="/author/suzan-verberne">Suzan Verberne</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 14</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/learning-to-use-tools-via-cooperative-and" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/learning-to-use-tools-via-cooperative-and#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/urbangpt-spatio-temporal-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/c3bee5aa-02a3-4414-9414-783ed32e02ed.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/urbangpt-spatio-temporal-large-language">UrbanGPT: Spatio-Temporal Large Language Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/urbangpt-spatio-temporal-large-language#code">2 code implementations</a> • <span class="author-name-text item-date-pub">25 Feb 2024</span> • <span class="author-span "> <a href="/author/zhonghang-li">Zhonghang Li</a></span>, <span class="author-span "> <a href="/author/lianghao-xia">Lianghao Xia</a></span>, <span class="author-span "> <a href="/author/jiabin-tang">Jiabin Tang</a></span>, <span class="author-span "> <a href="/author/yong-xu">Yong Xu</a></span>, <span class="author-span "> <a href="/author/lei-shi">Lei Shi</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">These findings highlight the potential of building large language models for spatio-temporal learning, particularly in zero-shot scenarios where labeled data is scarce.</p> <div class="sota"> </div> <p> <a href="/task/10-shot-image-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>10-shot image generation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 290</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/urbangpt-spatio-temporal-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/urbangpt-spatio-temporal-large-language#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/higpt-heterogeneous-graph-language-model"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/64e942bc-2a9e-404b-b699-8c53d444dad2.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/higpt-heterogeneous-graph-language-model">HiGPT: Heterogeneous Graph Language Model</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/higpt-heterogeneous-graph-language-model#code">1 code implementation</a> • <span class="author-name-text item-date-pub">25 Feb 2024</span> • <span class="author-span "> <a href="/author/jiabin-tang">Jiabin Tang</a></span>, <span class="author-span "> <a href="/author/yuhao-yang">Yuhao Yang</a></span>, <span class="author-span "> <a href="/author/wei-wei">Wei Wei</a></span>, <span class="author-span "> <a href="/author/lei-shi">Lei Shi</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets.</p> <div class="sota"> </div> <p> <a href="/task/graph-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Learning</span> </span> </a> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/higpt-heterogeneous-graph-language-model#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 110</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/higpt-heterogeneous-graph-language-model" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/higpt-heterogeneous-graph-language-model#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/the-good-and-the-bad-exploring-privacy-issues"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/e7f3ce92-aa3d-47db-9f20-3c2acc53d24a.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/the-good-and-the-bad-exploring-privacy-issues">The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/the-good-and-the-bad-exploring-privacy-issues#code">1 code implementation</a> • <span class="author-name-text item-date-pub">23 Feb 2024</span> • <span class="author-span "> <a href="/author/shenglai-zeng">Shenglai Zeng</a></span>, <span class="author-span "> <a href="/author/jiankun-zhang">Jiankun Zhang</a></span>, <span class="author-span "> <a href="/author/pengfei-he">Pengfei He</a></span>, <span class="author-span "> <a href="/author/yue-xing">Yue Xing</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/han-xu">Han Xu</a></span>, <span class="author-span "> <a href="/author/jie-ren">Jie Ren</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">In this work, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a href="/task/rag"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/2554a77c-c5ba-475b-bfba-b70a2b41a551.jpg"> <span>RAG</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/the-good-and-the-bad-exploring-privacy-issues#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 37</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/the-good-and-the-bad-exploring-privacy-issues" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/the-good-and-the-bad-exploring-privacy-issues#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/knowtuning-knowledge-aware-fine-tuning-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/cca30cf4-4975-41c0-a81d-3c22c49b4416.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/knowtuning-knowledge-aware-fine-tuning-for">KnowTuning: Knowledge-aware Fine-tuning for Large Language Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/knowtuning-knowledge-aware-fine-tuning-for#code">2 code implementations</a> • <span class="author-name-text item-date-pub">17 Feb 2024</span> • <span class="author-span "> <a href="/author/yougang-lyu">Yougang Lyu</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/haibo-shi">Haibo Shi</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/pengjie-ren">Pengjie Ren</a></span>, <span class="author-span "> <a href="/author/zhumin-chen">Zhumin Chen</a></span>, <span class="author-span "> <a href="/author/maarten-de-rijke">Maarten de Rijke</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">To address these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs.</p> <div class="sota"> </div> <p> <a href="/task/question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/56ae901a-265f-415f-b175-ce54133d648b.jpg"> <span>Question Answering</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 34,987</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/knowtuning-knowledge-aware-fine-tuning-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/knowtuning-knowledge-aware-fine-tuning-for#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/the-butterfly-effect-of-model-editing-few"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2402.09656.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/the-butterfly-effect-of-model-editing-few">The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/the-butterfly-effect-of-model-editing-few#code">1 code implementation</a> • <span class="author-name-text item-date-pub">15 Feb 2024</span> • <span class="author-span "> <a href="/author/wanli-yang">Wanli Yang</a></span>, <span class="author-span "> <a href="/author/fei-sun">Fei Sun</a></span>, <span class="author-span "> <a href="/author/xinyu-ma">Xinyu Ma</a></span>, <span class="author-span "> <a href="/author/xun-liu">Xun Liu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/xueqi-cheng">Xueqi Cheng</a></span> </p> <p class="item-strip-abstract">In this work, we reveal a critical phenomenon: even a single edit can trigger model collapse, manifesting as significant performance degradation in various benchmark tasks.</p> <div class="sota"> </div> <p> <a href="/task/benchmarking"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Benchmarking</span> </span> </a> <a href="/task/model-editing"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Model Editing</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 9</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/the-butterfly-effect-of-model-editing-few" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/the-butterfly-effect-of-model-editing-few#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/vislinginstruct-elevating-zero-shot-learning"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2402.07398.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/vislinginstruct-elevating-zero-shot-learning">VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/vislinginstruct-elevating-zero-shot-learning#code">1 code implementation</a> • <span class="author-name-text item-date-pub">12 Feb 2024</span> • <span class="author-span "> <a href="/author/dongsheng-zhu">Dongsheng Zhu</a></span>, <span class="author-span "> <a href="/author/xunzhu-tang">Xunzhu Tang</a></span>, <span class="author-span "> <a href="/author/weidong-han">Weidong Han</a></span>, <span class="author-span "> <a href="/author/jinghui-lu">Jinghui Lu</a></span>, <span class="author-span "> <a href="/author/yukun-zhao">Yukun Zhao</a></span>, <span class="author-span "> <a href="/author/guoliang-xing">Guoliang Xing</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning.</p> <div class="sota"> </div> <p> <a href="/task/in-context-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>In-Context Learning</span> </span> </a> <a href="/task/zero-shot-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000158-1446b13d_bCNDpWB.jpg"> <span>Zero-Shot Learning</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 7</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/vislinginstruct-elevating-zero-shot-learning" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/vislinginstruct-elevating-zero-shot-learning#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/text-video-retrieval-via-variational-multi"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2401.03177.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/text-video-retrieval-via-variational-multi">Text-Video Retrieval via Variational Multi-Modal Hypergraph Networks</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/text-video-retrieval-via-variational-multi#code">no code implementations</a> • <span class="author-name-text item-date-pub">6 Jan 2024</span> • <span class="author-span "> <a href="/author/qian-li">Qian Li</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/hengzhu-tang">Hengzhu Tang</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.</p> <div class="sota"> </div> <p> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> <a href="/task/variational-inference"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Variational Inference</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/text-video-retrieval-via-variational-multi#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/text-video-retrieval-via-variational-multi" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/text-video-retrieval-via-variational-multi#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/agent4ranking-semantic-robust-ranking-via"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2312.15450.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/agent4ranking-semantic-robust-ranking-via">Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/agent4ranking-semantic-robust-ranking-via#code">no code implementations</a> • <span class="author-name-text item-date-pub">24 Dec 2023</span> • <span class="author-span "> <a href="/author/xiaopeng-li">Xiaopeng Li</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/pengyue-jia">Pengyue Jia</a></span>, <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/agent4ranking-semantic-robust-ranking-via" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/agent4ranking-semantic-robust-ranking-via#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/towards-verifiable-text-generation-with-1"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2312.09075.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/towards-verifiable-text-generation-with-1">Towards Verifiable Text Generation with Evolving Memory and Self-Reflection</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/towards-verifiable-text-generation-with-1#code">no code implementations</a> • <span class="author-name-text item-date-pub">14 Dec 2023</span> • <span class="author-span "> <a href="/author/hao-sun">Hao Sun</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/bo-wang-1">Bo wang</a></span>, <span class="author-span "> <a href="/author/yingyan-hou">Yingyan Hou</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/yan-zhang">Yan Zhang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also known as hallucination.</p> <div class="sota"> </div> <p> <a href="/task/hallucination"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Hallucination</span> </span> </a> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/towards-verifiable-text-generation-with-1#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/towards-verifiable-text-generation-with-1" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/towards-verifiable-text-generation-with-1#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/instruction-distillation-makes-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2311.01555.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/instruction-distillation-makes-large-language">Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/instruction-distillation-makes-large-language#code">1 code implementation</a> • <span class="author-name-text item-date-pub">2 Nov 2023</span> • <span class="author-span "> <a href="/author/weiwei-sun">Weiwei Sun</a></span>, <span class="author-span "> <a href="/author/zheng-chen">Zheng Chen</a></span>, <span class="author-span "> <a href="/author/xinyu-ma">Xinyu Ma</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/pengjie-ren">Pengjie Ren</a></span>, <span class="author-span "> <a href="/author/zhumin-chen">Zhumin Chen</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods.</p> <div class="sota"> </div> <p> <a href="/task/prompt-engineering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/5caee997-0bf0-4bdd-bedc-d0612e504c24.jpg"> <span>Prompt Engineering</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 529</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/instruction-distillation-makes-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/instruction-distillation-makes-large-language#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/llmrec-large-language-models-with-graph"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/e172c6da-d7b5-4731-94a2-bea56fd636a5.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/llmrec-large-language-models-with-graph">LLMRec: Large Language Models with Graph Augmentation for Recommendation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/llmrec-large-language-models-with-graph#code">1 code implementation</a> • <span class="author-name-text item-date-pub">1 Nov 2023</span> • <span class="author-span "> <a href="/author/wei-wei">Wei Wei</a></span>, <span class="author-span "> <a href="/author/xubin-ren">Xubin Ren</a></span>, <span class="author-span "> <a href="/author/jiabin-tang">Jiabin Tang</a></span>, <span class="author-span "> <a href="/author/qinyong-wang">Qinyong Wang</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders.</p> <div class="sota"> </div> <p> <a href="/task/model-optimization"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Model Optimization</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 389</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/llmrec-large-language-models-with-graph" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/llmrec-large-language-models-with-graph#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/mill-mutual-verification-with-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2310.19056.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/mill-mutual-verification-with-large-language">MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/mill-mutual-verification-with-large-language#code">1 code implementation</a> • <span class="author-name-text item-date-pub">29 Oct 2023</span> • <span class="author-span "> <a href="/author/pengyue-jia">Pengyue Jia</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/xiaopeng-li">Xiaopeng Li</a></span>, <span class="author-span "> <a href="/author/changying-hao">Changying Hao</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/mill-mutual-verification-with-large-language#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/mill-mutual-verification-with-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/mill-mutual-verification-with-large-language#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/embedding-in-recommender-systems-a-survey"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2310.18608.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/embedding-in-recommender-systems-a-survey">Embedding in Recommender Systems: A Survey</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/embedding-in-recommender-systems-a-survey#code">1 code implementation</a> • <span class="author-name-text item-date-pub">28 Oct 2023</span> • <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/maolin-wang">Maolin Wang</a></span>, <span class="author-span "> <a href="/author/xinjian-zhao">Xinjian Zhao</a></span>, <span class="author-span "> <a href="/author/jiansheng-li">Jiansheng Li</a></span>, <span class="author-span "> <a href="/author/shucheng-zhou">Shucheng Zhou</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/qing-li">Qing Li</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span "> <a href="/author/ruocheng-guo">Ruocheng Guo</a></span> </p> <p class="item-strip-abstract">This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.</p> <div class="sota"> </div> <p> <a href="/task/automl"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>AutoML</span> </span> </a> <a href="/task/collaborative-filtering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000595-96a2d3eb.jpg"> <span>Collaborative Filtering</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/embedding-in-recommender-systems-a-survey#tasks"> <span class="badge badge-primary"> <b>+4</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 21</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/embedding-in-recommender-systems-a-survey" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/embedding-in-recommender-systems-a-survey#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/knowing-what-llms-do-not-know-a-simple-yet"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2310.17918.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/knowing-what-llms-do-not-know-a-simple-yet">Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/knowing-what-llms-do-not-know-a-simple-yet#code">no code implementations</a> • <span class="author-name-text item-date-pub">27 Oct 2023</span> • <span class="author-span "> <a href="/author/yukun-zhao">Yukun Zhao</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/weiwei-sun">Weiwei Sun</a></span>, <span class="author-span "> <a href="/author/guoliang-xing">Guoliang Xing</a></span>, <span class="author-span "> <a href="/author/chong-meng">Chong Meng</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/knowing-what-llms-do-not-know-a-simple-yet" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/knowing-what-llms-do-not-know-a-simple-yet#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/enhancing-graph-neural-networks-with"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2310.17394.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/enhancing-graph-neural-networks-with">PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/enhancing-graph-neural-networks-with#code">1 code implementation</a> • <span class="author-name-text item-date-pub">26 Oct 2023</span> • <span class="author-span "> <a href="/author/qingqing-ge">Qingqing Ge</a></span>, <span class="author-span "> <a href="/author/zeyuan-zhao">Zeyuan Zhao</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/anfeng-cheng">Anfeng Cheng</a></span>, <span class="author-span "> <a href="/author/xiang-li">Xiang Li</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In particular, PSP 1) employs a dual-view contrastive learning to align the latent semantic spaces of node attributes and graph structure, and 2) incorporates structure information in prompted graph to construct more accurate prototype vectors and elicit more pre-trained knowledge in prompt tuning.</p> <div class="sota"> </div> <p> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a href="/task/graph-classification"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/2ea42743-71c0-4f81-995d-ce9dc4bd63b0.jpg"> <span>Graph Classification</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/enhancing-graph-neural-networks-with#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 1</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/enhancing-graph-neural-networks-with" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/enhancing-graph-neural-networks-with#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/diqad-a-benchmark-dataset-for-end-to-end-open"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2310.16319.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/diqad-a-benchmark-dataset-for-end-to-end-open">DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/diqad-a-benchmark-dataset-for-end-to-end-open#code">no code implementations</a> • <span class="author-name-text item-date-pub">25 Oct 2023</span> • <span class="author-span "> <a href="/author/yukun-zhao">Yukun Zhao</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/weiwei-sun">Weiwei Sun</a></span>, <span class="author-span "> <a href="/author/chong-meng">Chong Meng</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Dialogue assessment plays a critical role in the development of open-domain dialogue systems.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/diqad-a-benchmark-dataset-for-end-to-end-open" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/diqad-a-benchmark-dataset-for-end-to-end-open#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/representation-learning-with-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/f0f6844d-664a-445a-9732-8ce4734e00eb.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/representation-learning-with-large-language">Representation Learning with Large Language Models for Recommendation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/representation-learning-with-large-language#code">1 code implementation</a> • <span class="author-name-text item-date-pub">24 Oct 2023</span> • <span class="author-span "> <a href="/author/xubin-ren">Xubin Ren</a></span>, <span class="author-span "> <a href="/author/wei-wei">Wei Wei</a></span>, <span class="author-span "> <a href="/author/lianghao-xia">Lianghao Xia</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/junfeng-wang">Junfeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework.</p> <div class="sota"> </div> <p> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a href="/task/representation-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000228-3131cfbf_nx72Tly.jpg"> <span>Representation Learning</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 344</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/representation-learning-with-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/representation-learning-with-large-language#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/graphgpt-graph-instruction-tuning-for-large"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/379c5e42-8be3-45cf-8464-f1773d2f718a.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/graphgpt-graph-instruction-tuning-for-large">GraphGPT: Graph Instruction Tuning for Large Language Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/graphgpt-graph-instruction-tuning-for-large#code">1 code implementation</a> • <span class="author-name-text item-date-pub">19 Oct 2023</span> • <span class="author-span "> <a href="/author/jiabin-tang">Jiabin Tang</a></span>, <span class="author-span "> <a href="/author/yuhao-yang">Yuhao Yang</a></span>, <span class="author-span "> <a href="/author/wei-wei">Wei Wei</a></span>, <span class="author-span "> <a href="/author/lei-shi">Lei Shi</a></span>, <span class="author-span "> <a href="/author/lixin-su">Lixin Su</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">The open-sourced model implementation of our GraphGPT is available at https://github. com/HKUDS/GraphGPT.</p> <div class="sota"> </div> <p> <a href="/task/data-augmentation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001560-029cbc00.jpg"> <span>Data Augmentation</span> </span> </a> <a href="/task/graph-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/graphgpt-graph-instruction-tuning-for-large#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 630</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graphgpt-graph-instruction-tuning-for-large" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/graphgpt-graph-instruction-tuning-for-large#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/exploring-memorization-in-fine-tuned-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2310.06714.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/exploring-memorization-in-fine-tuned-language">Exploring Memorization in Fine-tuned Language Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/exploring-memorization-in-fine-tuned-language#code">no code implementations</a> • <span class="author-name-text item-date-pub">10 Oct 2023</span> • <span class="author-span "> <a href="/author/shenglai-zeng">Shenglai Zeng</a></span>, <span class="author-span "> <a href="/author/yaxin-li">Yaxin Li</a></span>, <span class="author-span "> <a href="/author/jie-ren">Jie Ren</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/han-xu">Han Xu</a></span>, <span class="author-span "> <a href="/author/pengfei-he">Pengfei He</a></span>, <span class="author-span "> <a href="/author/yue-xing">Yue Xing</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In this work, we conduct the first comprehensive analysis to explore language models' (LMs) memorization during fine-tuning across tasks.</p> <div class="sota"> </div> <p> <a href="/task/memorization"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Memorization</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/exploring-memorization-in-fine-tuned-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/exploring-memorization-in-fine-tuned-language#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/aligning-the-capabilities-of-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2309.17078.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/aligning-the-capabilities-of-large-language">Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/aligning-the-capabilities-of-large-language#code">no code implementations</a> • <span class="author-name-text item-date-pub">29 Sep 2023</span> • <span class="author-span "> <a href="/author/qian-dong">Qian Dong</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/qingyao-ai">Qingyao Ai</a></span>, <span class="author-span "> <a href="/author/zhijing-wu">Zhijing Wu</a></span>, <span class="author-span "> <a href="/author/haitao-li">Haitao Li</a></span>, <span class="author-span "> <a href="/author/yiqun-liu">Yiqun Liu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/shaoping-ma">Shaoping Ma</a></span> </p> <p class="item-strip-abstract">Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR).</p> <div class="sota"> </div> <p> <a href="/task/data-augmentation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001560-029cbc00.jpg"> <span>Data Augmentation</span> </span> </a> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/aligning-the-capabilities-of-large-language#tasks"> <span class="badge badge-primary"> <b>+4</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/aligning-the-capabilities-of-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/aligning-the-capabilities-of-large-language#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/explainability-for-large-language-models-a"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2309.01029.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/explainability-for-large-language-models-a">Explainability for Large Language Models: A Survey</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/explainability-for-large-language-models-a#code">no code implementations</a> • <span class="author-name-text item-date-pub">2 Sep 2023</span> • <span class="author-span "> <a href="/author/haiyan-zhao">Haiyan Zhao</a></span>, <span class="author-span "> <a href="/author/hanjie-chen">Hanjie Chen</a></span>, <span class="author-span "> <a href="/author/fan-yang">Fan Yang</a></span>, <span class="author-span "> <a href="/author/ninghao-liu">Ninghao Liu</a></span>, <span class="author-span "> <a href="/author/huiqi-deng">Huiqi Deng</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/mengnan-du">Mengnan Du</a></span> </p> <p class="item-strip-abstract">For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge.</p> <div class="sota"> </div> <p> <a href="/task/survey"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Survey</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/explainability-for-large-language-models-a" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/explainability-for-large-language-models-a#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/information-retrieval-meets-large-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2307.09751.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/information-retrieval-meets-large-language">Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/information-retrieval-meets-large-language#code">no code implementations</a> • <span class="author-name-text item-date-pub">19 Jul 2023</span> • <span class="author-span "> <a href="/author/qingyao-ai">Qingyao Ai</a></span>, <span class="author-span "> <a href="/author/ting-bai">Ting Bai</a></span>, <span class="author-span "> <a href="/author/zhao-cao">Zhao Cao</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span>, <span class="author-span "> <a href="/author/jiawei-chen">Jiawei Chen</a></span>, <span class="author-span "> <a href="/author/zhumin-chen">Zhumin Chen</a></span>, <span class="author-span "> <a href="/author/zhiyong-cheng">Zhiyong Cheng</a></span>, <span class="author-span "> <a href="/author/shoubin-dong">Shoubin Dong</a></span>, <span class="author-span "> <a href="/author/zhicheng-dou">Zhicheng Dou</a></span>, <span class="author-span "> <a href="/author/fuli-feng">Fuli Feng</a></span>, <span class="author-span "> <a href="/author/shen-gao">Shen Gao</a></span>, <span class="author-span "> <a href="/author/jiafeng-guo">Jiafeng Guo</a></span>, <span class="author-span "> <a href="/author/xiangnan-he">Xiangnan He</a></span>, <span class="author-span "> <a href="/author/yanyan-lan">Yanyan Lan</a></span>, <span class="author-span "> <a href="/author/chenliang-li">Chenliang Li</a></span>, <span class="author-span "> <a href="/author/yiqun-liu">Yiqun Liu</a></span>, <span class="author-span "> <a href="/author/ziyu-lyu">Ziyu Lyu</a></span>, <span class="author-span "> <a href="/author/weizhi-ma">Weizhi Ma</a></span>, <span class="author-span "> <a href="/author/jun-ma">Jun Ma</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span "> <a href="/author/pengjie-ren">Pengjie Ren</a></span>, <span class="author-span "> <a href="/author/zhiqiang-wang">Zhiqiang Wang</a></span>, <span class="author-span "> <a href="/author/mingwen-wang">Mingwen Wang</a></span>, <span class="author-span "> <a href="/author/ji-rong-wen">Ji-Rong Wen</a></span>, <span class="author-span "> <a href="/author/le-wu">Le Wu</a></span>, <span class="author-span "> <a href="/author/xin-xin">Xin Xin</a></span>, <span class="author-span "> <a href="/author/jun-xu">Jun Xu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/peng-zhang">Peng Zhang</a></span>, <span class="author-span "> <a href="/author/fan-zhang">Fan Zhang</a></span>, <span class="author-span "> <a href="/author/weinan-zhang">Weinan Zhang</a></span>, <span class="author-span "> <a href="/author/min-zhang">Min Zhang</a></span>, <span class="author-span "> <a href="/author/xiaofei-zhu">Xiaofei Zhu</a></span> </p> <p class="item-strip-abstract">The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/information-retrieval-meets-large-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/information-retrieval-meets-large-language#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/exploring-the-potential-of-large-language-1"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/ea4af220-9554-48bc-9e98-be4dccef4fba.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/exploring-the-potential-of-large-language-1">Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/exploring-the-potential-of-large-language-1#code">2 code implementations</a> • <span class="author-name-text item-date-pub">7 Jul 2023</span> • <span class="author-span "> <a href="/author/zhikai-chen">Zhikai Chen</a></span>, <span class="author-span "> <a href="/author/haitao-mao">Haitao Mao</a></span>, <span class="author-span "> <a href="/author/hang-li">Hang Li</a></span>, <span class="author-span "> <a href="/author/wei-jin">Wei Jin</a></span>, <span class="author-span "> <a href="/author/hongzhi-wen">Hongzhi Wen</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/wenqi-fan">Wenqi Fan</a></span>, <span class="author-span "> <a href="/author/hui-liu">Hui Liu</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.</p> <div class="sota"> </div> <p> <a href="/task/general-knowledge"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>General Knowledge</span> </span> </a> <a href="/task/node-classification"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000407-3df5d6f0.jpg"> <span>Node Classification</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 255</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/exploring-the-potential-of-large-language-1" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/exploring-the-potential-of-large-language-1#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/evaluating-graph-neural-networks-for-link"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2306.10453.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/evaluating-graph-neural-networks-for-link">Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/evaluating-graph-neural-networks-for-link#code">1 code implementation</a> • <span class="item-conference-link"> <a href="/conference/neurips-2023-11"> NeurIPS 2023 </a> </span> • <span class="author-span "> <a href="/author/juanhui-li">Juanhui Li</a></span>, <span class="author-span "> <a href="/author/harry-shomer">Harry Shomer</a></span>, <span class="author-span "> <a href="/author/haitao-mao">Haitao Mao</a></span>, <span class="author-span "> <a href="/author/shenglai-zeng">Shenglai Zeng</a></span>, <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span "> <a href="/author/neil-shah">Neil Shah</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models.</p> <div class="sota"> </div> <p> <a href="/task/benchmarking"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Benchmarking</span> </span> </a> <a href="/task/link-prediction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000031-326cd034.jpg"> <span>Link Prediction</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 44</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/evaluating-graph-neural-networks-for-link" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/evaluating-graph-neural-networks-for-link#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/i-3-retriever-incorporating-implicit"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2306.02371.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/i-3-retriever-incorporating-implicit">I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/i-3-retriever-incorporating-implicit#code">1 code implementation</a> • <span class="author-name-text item-date-pub">4 Jun 2023</span> • <span class="author-span "> <a href="/author/qian-dong">Qian Dong</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/qingyao-ai">Qingyao Ai</a></span>, <span class="author-span "> <a href="/author/haitao-li">Haitao Li</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/yiqun-liu">Yiqun Liu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/shaoping-ma">Shaoping Ma</a></span> </p> <p class="item-strip-abstract">Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance.</p> <div class="sota"> </div> <p> <a href="/task/knowledge-distillation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Distillation</span> </span> </a> <a href="/task/passage-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Passage Retrieval</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/i-3-retriever-incorporating-implicit#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 48</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/i-3-retriever-incorporating-implicit" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/i-3-retriever-incorporating-implicit#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/pretrained-language-model-based-web-search"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2306.01599.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/pretrained-language-model-based-web-search">Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/pretrained-language-model-based-web-search#code">no code implementations</a> • <span class="author-name-text item-date-pub">2 Jun 2023</span> • <span class="author-span "> <a href="/author/canjia-li">Canjia Li</a></span>, <span class="author-span "> <a href="/author/xiaoyang-wang">Xiaoyang Wang</a></span>, <span class="author-span "> <a href="/author/dongdong-li">Dongdong Li</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/yu-lu">Yu Lu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/simiu-gu">Simiu Gu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/pretrained-language-model-based-web-search" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/pretrained-language-model-based-web-search#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/semantic-enhanced-differentiable-search-index"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2305.15115.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/semantic-enhanced-differentiable-search-index">Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/semantic-enhanced-differentiable-search-index#code">no code implementations</a> • <span class="author-name-text item-date-pub">24 May 2023</span> • <span class="author-span "> <a href="/author/yubao-tang">Yubao Tang</a></span>, <span class="author-span "> <a href="/author/ruqing-zhang">Ruqing Zhang</a></span>, <span class="author-span "> <a href="/author/jiafeng-guo">Jiafeng Guo</a></span>, <span class="author-span "> <a href="/author/jiangui-chen">Jiangui Chen</a></span>, <span class="author-span "> <a href="/author/zuowei-zhu">Zuowei Zhu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/xueqi-cheng">Xueqi Cheng</a></span> </p> <p class="item-strip-abstract">Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning.</p> <div class="sota"> </div> <p> <a href="/task/memorization"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Memorization</span> </span> </a> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/semantic-enhanced-differentiable-search-index" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/semantic-enhanced-differentiable-search-index#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/unconfounded-propensity-estimation-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2305.09918.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/unconfounded-propensity-estimation-for">Unconfounded Propensity Estimation for Unbiased Ranking</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/unconfounded-propensity-estimation-for#code">no code implementations</a> • <span class="author-name-text item-date-pub">17 May 2023</span> • <span class="author-span "> <a href="/author/dan-luo">Dan Luo</a></span>, <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/qingyao-ai">Qingyao Ai</a></span>, <span class="author-span "> <a href="/author/zhiyu-chen">Zhiyu Chen</a></span>, <span class="author-span "> <a href="/author/chenliang-li">Chenliang Li</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/brian-d-davison">Brian D. Davison</a></span> </p> <p class="item-strip-abstract">The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems.</p> <div class="sota"> </div> <p> <a href="/task/learning-to-rank"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Learning-To-Rank</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/unconfounded-propensity-estimation-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/unconfounded-propensity-estimation-for#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/boosting-event-extraction-with-denoised"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2305.09598.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/boosting-event-extraction-with-denoised">Boosting Event Extraction with Denoised Structure-to-Text Augmentation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/boosting-event-extraction-with-denoised#code">no code implementations</a> • <span class="author-name-text item-date-pub">16 May 2023</span> • <span class="author-span "> <a href="/author/bo-wang-1">Bo wang</a></span>, <span class="author-span "> <a href="/author/heyan-huang">Heyan Huang</a></span>, <span class="author-span "> <a href="/author/xiaochi-wei">Xiaochi Wei</a></span>, <span class="author-span "> <a href="/author/ge-shi">Ge Shi</a></span>, <span class="author-span "> <a href="/author/xiao-liu">Xiao Liu</a></span>, <span class="author-span "> <a href="/author/chong-feng">Chong Feng</a></span>, <span class="author-span "> <a href="/author/tong-zhou">Tong Zhou</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations.</p> <div class="sota"> </div> <p> <a href="/task/deep-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Deep Reinforcement Learning</span> </span> </a> <a href="/task/event-extraction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Event Extraction</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/boosting-event-extraction-with-denoised#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/boosting-event-extraction-with-denoised" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/boosting-event-extraction-with-denoised#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/disentangled-contrastive-collaborative"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2305.02759.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/disentangled-contrastive-collaborative">Disentangled Contrastive Collaborative Filtering</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/disentangled-contrastive-collaborative#code">1 code implementation</a> • <span class="author-name-text item-date-pub">4 May 2023</span> • <span class="author-span "> <a href="/author/xubin-ren">Xubin Ren</a></span>, <span class="author-span "> <a href="/author/lianghao-xia">Lianghao Xia</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span> </p> <p class="item-strip-abstract">Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF).</p> <div class="sota"> </div> <p> <a href="/task/collaborative-filtering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000595-96a2d3eb.jpg"> <span>Collaborative Filtering</span> </span> </a> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/disentangled-contrastive-collaborative#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 58</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/disentangled-contrastive-collaborative" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/disentangled-contrastive-collaborative#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/is-chatgpt-good-at-search-investigating-large"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/a2c75c97-2172-43b6-ae6a-76e853e997dd.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/is-chatgpt-good-at-search-investigating-large">Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/is-chatgpt-good-at-search-investigating-large#code">1 code implementation</a> • <span class="author-name-text item-date-pub">19 Apr 2023</span> • <span class="author-span "> <a href="/author/weiwei-sun">Weiwei Sun</a></span>, <span class="author-span "> <a href="/author/lingyong-yan">Lingyong Yan</a></span>, <span class="author-span "> <a href="/author/xinyu-ma">Xinyu Ma</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/pengjie-ren">Pengjie Ren</a></span>, <span class="author-span "> <a href="/author/zhumin-chen">Zhumin Chen</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">In this paper, we first investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR.</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/passage-ranking"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Passage Ranking</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/is-chatgpt-good-at-search-investigating-large#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 529</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/is-chatgpt-good-at-search-investigating-large" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/is-chatgpt-good-at-search-investigating-large#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/user-retention-oriented-recommendation-with"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2303.06347.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/user-retention-oriented-recommendation-with">User Retention-oriented Recommendation with Decision Transformer</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/user-retention-oriented-recommendation-with#code">1 code implementation</a> • <span class="author-name-text item-date-pub">11 Mar 2023</span> • <span class="author-span "> <a href="/author/kesen-zhao">Kesen Zhao</a></span>, <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/maolin-wang">Maolin Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation.</p> <div class="sota"> </div> <p> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a href="/task/counterfactual"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>counterfactual</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/user-retention-oriented-recommendation-with#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 17</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/user-retention-oriented-recommendation-with" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/user-retention-oriented-recommendation-with#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/layout-aware-webpage-quality-assessment"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2301.12152.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/layout-aware-webpage-quality-assessment">Layout-aware Webpage Quality Assessment</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/layout-aware-webpage-quality-assessment#code">no code implementations</a> • <span class="author-name-text item-date-pub">28 Jan 2023</span> • <span class="author-span "> <a href="/author/anfeng-cheng">Anfeng Cheng</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/weibin-li">Weibin Li</a></span>, <span class="author-span "> <a href="/author/qian-dong">Qian Dong</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhengjie-huang">Zhengjie Huang</a></span>, <span class="author-span "> <a href="/author/shikun-feng">Shikun Feng</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner.</p> <div class="sota"> </div> <p> <a href="/task/graph-neural-network"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Neural Network</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/layout-aware-webpage-quality-assessment" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/layout-aware-webpage-quality-assessment#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/feature-level-debiased-natural-language"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2212.05421.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/feature-level-debiased-natural-language">Feature-Level Debiased Natural Language Understanding</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/feature-level-debiased-natural-language#code">1 code implementation</a> • <span class="author-name-text item-date-pub">11 Dec 2022</span> • <span class="author-span "> <a href="/author/yougang-lyu">Yougang Lyu</a></span>, <span class="author-span "> <a href="/author/piji-li">Piji Li</a></span>, <span class="author-span "> <a href="/author/yechang-yang">Yechang Yang</a></span>, <span class="author-span "> <a href="/author/maarten-de-rijke">Maarten de Rijke</a></span>, <span class="author-span "> <a href="/author/pengjie-ren">Pengjie Ren</a></span>, <span class="author-span "> <a href="/author/yukun-zhao">Yukun Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span> </p> <p class="item-strip-abstract">We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples.</p> <div class="sota"> </div> <p> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a href="/task/natural-language-understanding"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Natural Language Understanding</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 5</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/feature-level-debiased-natural-language" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/feature-level-debiased-natural-language#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/pile-pairwise-iterative-logits-ensemble-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2211.06059.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/pile-pairwise-iterative-logits-ensemble-for">PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/pile-pairwise-iterative-logits-ensemble-for#code">no code implementations</a> • <span class="author-name-text item-date-pub">11 Nov 2022</span> • <span class="author-span "> <a href="/author/lianshang-cai">Lianshang Cai</a></span>, <span class="author-span "> <a href="/author/linhao-zhang">Linhao Zhang</a></span>, <span class="author-span "> <a href="/author/dehong-ma">Dehong Ma</a></span>, <span class="author-span "> <a href="/author/jun-fan">Jun Fan</a></span>, <span class="author-span "> <a href="/author/daiting-shi">Daiting Shi</a></span>, <span class="author-span "> <a href="/author/yi-wu">Yi Wu</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/simiu-gu">Simiu Gu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In this paper, we focus on two key questions in knowledge distillation for ranking models: 1) how to ensemble knowledge from multi-teacher; 2) how to utilize the label information of data in the distillation process.</p> <div class="sota"> </div> <p> <a href="/task/knowledge-distillation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Distillation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/pile-pairwise-iterative-logits-ensemble-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/pile-pairwise-iterative-logits-ensemble-for#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/whole-page-unbiased-learning-to-rank"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2210.10718.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/whole-page-unbiased-learning-to-rank">Whole Page Unbiased Learning to Rank</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/whole-page-unbiased-learning-to-rank#code">no code implementations</a> • <span class="author-name-text item-date-pub">19 Oct 2022</span> • <span class="author-span "> <a href="/author/haitao-mao">Haitao Mao</a></span>, <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/yujia-zheng">Yujia Zheng</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span "> <a href="/author/xiaokai-chu">Xiaokai Chu</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span "> <a href="/author/qian-wang">Qian Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, named BAL, to automatically find the user behavior model with causal discovery and mitigate the biases induced by multiple SERP features with no specific design.</p> <div class="sota"> </div> <p> <a href="/task/causal-discovery"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001331-4fb576b7.jpg"> <span>Causal Discovery</span> </span> </a> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/whole-page-unbiased-learning-to-rank#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/whole-page-unbiased-learning-to-rank" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/whole-page-unbiased-learning-to-rank#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/cps-mebr-click-feedback-aware-web-page"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2210.09787.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/cps-mebr-click-feedback-aware-web-page">CPS-MEBR: Click Feedback-Aware Web Page Summarization for Multi-Embedding-Based Retrieval</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/cps-mebr-click-feedback-aware-web-page#code">no code implementations</a> • <span class="author-name-text item-date-pub">18 Oct 2022</span> • <span class="author-span "> <a href="/author/wenbiao-li">Wenbiao Li</a></span>, <span class="author-span "> <a href="/author/pan-tang">Pan Tang</a></span>, <span class="author-span "> <a href="/author/zhengfan-wu">Zhengfan Wu</a></span>, <span class="author-span "> <a href="/author/weixue-lu">Weixue Lu</a></span>, <span class="author-span "> <a href="/author/minghua-zhang">Minghua Zhang</a></span>, <span class="author-span "> <a href="/author/zhenlei-tian">Zhenlei Tian</a></span>, <span class="author-span "> <a href="/author/daiting-shi">Daiting Shi</a></span>, <span class="author-span "> <a href="/author/yu-sun">Yu Sun</a></span>, <span class="author-span "> <a href="/author/simiu-gu">Simiu Gu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Meanwhile, we introduce sentence-level semantic interaction to design a multi-embedding-based retrieval (MEBR) model, which can generate multiple embeddings to deal with different potential queries by using frequently clicked sentences in web pages.</p> <div class="sota"> </div> <p> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> <a href="/task/sentence"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Sentence</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/cps-mebr-click-feedback-aware-web-page" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/cps-mebr-click-feedback-aware-web-page#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/approximated-doubly-robust-search-relevance"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2208.07671.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/approximated-doubly-robust-search-relevance">Approximated Doubly Robust Search Relevance Estimation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/approximated-doubly-robust-search-relevance#code">no code implementations</a> • <span class="author-name-text item-date-pub">16 Aug 2022</span> • <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/changying-hao">Changying Hao</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/wenwen-ye">Wenwen Ye</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/simiu-gu">Simiu Gu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">We further instantiate the proposed unbiased relevance estimation framework in Baidu search, with comprehensive practical solutions designed regarding the data pipeline for click behavior tracking and online relevance estimation with an approximated deep neural network.</p> <div class="sota"> </div> <p> <a href="/task/counterfactual"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>counterfactual</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/approximated-doubly-robust-search-relevance" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/approximated-doubly-robust-search-relevance#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/model-based-unbiased-learning-to-rank"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2207.11785.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/model-based-unbiased-learning-to-rank">Model-based Unbiased Learning to Rank</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/model-based-unbiased-learning-to-rank#code">1 code implementation</a> • <span class="author-name-text item-date-pub">24 Jul 2022</span> • <span class="author-span "> <a href="/author/dan-luo">Dan Luo</a></span>, <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/qingyao-ai">Qingyao Ai</a></span>, <span class="author-span "> <a href="/author/zhiyu-chen">Zhiyu Chen</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/brian-d-davison">Brian D. Davison</a></span> </p> <p class="item-strip-abstract">Existing methods in unbiased learning to rank typically rely on click modeling or inverse propensity weighting (IPW).</p> <div class="sota"> </div> <p> <a href="/task/information-retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001451-80b2d378.jpg"> <span>Information Retrieval</span> </span> </a> <a href="/task/learning-to-rank"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Learning-To-Rank</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/model-based-unbiased-learning-to-rank#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 7</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/model-based-unbiased-learning-to-rank" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/model-based-unbiased-learning-to-rank#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/factorized-and-controllable-neural-re"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2207.06899.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/factorized-and-controllable-neural-re">Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/factorized-and-controllable-neural-re#code">no code implementations</a> • <span class="author-name-text item-date-pub">14 Jul 2022</span> • <span class="author-span "> <a href="/author/boming-zhao">Boming Zhao</a></span>, <span class="author-span "> <a href="/author/bangbang-yang">Bangbang Yang</a></span>, <span class="author-span "> <a href="/author/zhenyang-li">Zhenyang Li</a></span>, <span class="author-span "> <a href="/author/zuoyue-li">Zuoyue Li</a></span>, <span class="author-span "> <a href="/author/guofeng-zhang">Guofeng Zhang</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/zhaopeng-cui">Zhaopeng Cui</a></span>, <span class="author-span "> <a href="/author/hujun-bao">Hujun Bao</a></span> </p> <p class="item-strip-abstract">Expanding an existing tourist photo from a partially captured scene to a full scene is one of the desired experiences for photography applications.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/factorized-and-controllable-neural-re" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/factorized-and-controllable-neural-re#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/a-large-scale-search-dataset-for-unbiased"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2207.03051.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/a-large-scale-search-dataset-for-unbiased">A Large Scale Search Dataset for Unbiased Learning to Rank</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/a-large-scale-search-dataset-for-unbiased#code">1 code implementation</a> • <span class="author-name-text item-date-pub">7 Jul 2022</span> • <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/haitao-mao">Haitao Mao</a></span>, <span class="author-span "> <a href="/author/xiaokai-chu">Xiaokai Chu</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span "> <a href="/author/wenwen-ye">Wenwen Ye</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms.</p> <div class="sota"> </div> <p> <a href="/task/causal-discovery"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001331-4fb576b7.jpg"> <span>Causal Discovery</span> </span> </a> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/a-large-scale-search-dataset-for-unbiased#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 60</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/a-large-scale-search-dataset-for-unbiased" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/a-large-scale-search-dataset-for-unbiased#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/geometry-contrastive-learning-on"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2206.12547.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/geometry-contrastive-learning-on">Geometry Contrastive Learning on Heterogeneous Graphs</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/geometry-contrastive-learning-on#code">1 code implementation</a> • <span class="author-name-text item-date-pub">25 Jun 2022</span> • <span class="author-span "> <a href="/author/shichao-zhu">Shichao Zhu</a></span>, <span class="author-span "> <a href="/author/chuan-zhou">Chuan Zhou</a></span>, <span class="author-span "> <a href="/author/anfeng-cheng">Anfeng Cheng</a></span>, <span class="author-span "> <a href="/author/shirui-pan">Shirui Pan</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/bin-wang">Bin Wang</a></span> </p> <p class="item-strip-abstract">Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.</p> <div class="sota"> </div> <p> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a href="/task/node-classification"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000407-3df5d6f0.jpg"> <span>Node Classification</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/geometry-contrastive-learning-on#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 1</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/geometry-contrastive-learning-on" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/geometry-contrastive-learning-on#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/are-graph-neural-networks-really-helpful-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2205.10652.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/are-graph-neural-networks-really-helpful-for">Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/are-graph-neural-networks-really-helpful-for#code">1 code implementation</a> • <span class="author-name-text item-date-pub">21 May 2022</span> • <span class="author-span "> <a href="/author/juanhui-li">Juanhui Li</a></span>, <span class="author-span "> <a href="/author/harry-shomer">Harry Shomer</a></span>, <span class="author-span "> <a href="/author/jiayuan-ding">Jiayuan Ding</a></span>, <span class="author-span "> <a href="/author/yiqi-wang">Yiqi Wang</a></span>, <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span "> <a href="/author/neil-shah">Neil Shah</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 14</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/are-graph-neural-networks-really-helpful-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/are-graph-neural-networks-really-helpful-for#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/practical-strategies-of-active-learning-to"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2205.10137.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/practical-strategies-of-active-learning-to">A Simple yet Effective Framework for Active Learning to Rank</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/practical-strategies-of-active-learning-to#code">no code implementations</a> • <span class="author-name-text item-date-pub">20 May 2022</span> • <span class="author-span "> <a href="/author/qingzhong-wang">Qingzhong Wang</a></span>, <span class="author-span "> <a href="/author/haifang-li">Haifang Li</a></span>, <span class="author-span "> <a href="/author/haoyi-xiong">Haoyi Xiong</a></span>, <span class="author-span "> <a href="/author/wen-wang">Wen Wang</a></span>, <span class="author-span "> <a href="/author/jiang-bian">Jiang Bian</a></span>, <span class="author-span "> <a href="/author/yu-lu">Yu Lu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/dejing-dou">Dejing Dou</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.</p> <div class="sota"> </div> <p> <a href="/task/active-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Active Learning</span> </span> </a> <a href="/task/learning-to-rank"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Learning-To-Rank</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/practical-strategies-of-active-learning-to" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/practical-strategies-of-active-learning-to#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/ernie-search-bridging-cross-encoder-with-dual"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2205.09153.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/ernie-search-bridging-cross-encoder-with-dual">ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/ernie-search-bridging-cross-encoder-with-dual#code">no code implementations</a> • <span class="author-name-text item-date-pub">18 May 2022</span> • <span class="author-span "> <a href="/author/yuxiang-lu">Yuxiang Lu</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/jiaxiang-liu">Jiaxiang Liu</a></span>, <span class="author-span "> <a href="/author/yunsheng-shi">Yunsheng Shi</a></span>, <span class="author-span "> <a href="/author/zhengjie-huang">Zhengjie Huang</a></span>, <span class="author-span "> <a href="/author/shikun-feng-yu-sun">Shikun Feng Yu Sun</a></span>, <span class="author-span "> <a href="/author/hao-tian">Hao Tian</a></span>, <span class="author-span "> <a href="/author/hua-wu-1">Hua Wu</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/haifeng-wang">Haifeng Wang</a></span> </p> <p class="item-strip-abstract">Our method 1) introduces a self on-the-fly distillation method that can effectively distill late interaction (i. e., ColBERT) to vanilla dual-encoder, and 2) incorporates a cascade distillation process to further improve the performance with a cross-encoder teacher.</p> <div class="sota"> </div> <p> <a href="/task/knowledge-distillation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Knowledge Distillation</span> </span> </a> <a href="/task/open-domain-question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/cfce648f-fcb6-4ada-aefe-d27cf9003d8d.jpg"> <span>Open-Domain Question Answering</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/ernie-search-bridging-cross-encoder-with-dual#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/ernie-search-bridging-cross-encoder-with-dual" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/ernie-search-bridging-cross-encoder-with-dual#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/hypergraph-contrastive-collaborative"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2204.12200.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/hypergraph-contrastive-collaborative">Hypergraph Contrastive Collaborative Filtering</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/hypergraph-contrastive-collaborative#code">1 code implementation</a> • <span class="author-name-text item-date-pub">26 Apr 2022</span> • <span class="author-span "> <a href="/author/lianghao-xia">Lianghao Xia</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span>, <span class="author-span "> <a href="/author/yong-xu">Yong Xu</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jimmy-xiangji-huang">Jimmy Xiangji Huang</a></span> </p> <p class="item-strip-abstract">Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination.</p> <div class="sota"> </div> <p> <a href="/task/collaborative-filtering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000595-96a2d3eb.jpg"> <span>Collaborative Filtering</span> </span> </a> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/hypergraph-contrastive-collaborative#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 92</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/hypergraph-contrastive-collaborative" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/hypergraph-contrastive-collaborative#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/incorporating-explicit-knowledge-in-pre"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2204.11673.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/incorporating-explicit-knowledge-in-pre">Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/incorporating-explicit-knowledge-in-pre#code">no code implementations</a> • <span class="author-name-text item-date-pub">25 Apr 2022</span> • <span class="author-span "> <a href="/author/qian-dong">Qian Dong</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span "> <a href="/author/shuzi-niu">Shuzi Niu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage.</p> <div class="sota"> </div> <p> <a href="/task/graph-neural-network"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Neural Network</span> </span> </a> <a href="/task/natural-language-understanding"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Natural Language Understanding</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/incorporating-explicit-knowledge-in-pre#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/incorporating-explicit-knowledge-in-pre" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/incorporating-explicit-knowledge-in-pre#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/graph-enhanced-bert-for-query-understanding"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2204.06522.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/graph-enhanced-bert-for-query-understanding">Graph Enhanced BERT for Query Understanding</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/graph-enhanced-bert-for-query-understanding#code">no code implementations</a> • <span class="author-name-text item-date-pub">3 Apr 2022</span> • <span class="author-span "> <a href="/author/juanhui-li">Juanhui Li</a></span>, <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span "> <a href="/author/wei-zeng">Wei Zeng</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graph-enhanced-bert-for-query-understanding" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/graph-enhanced-bert-for-query-understanding#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/sequential-recommendation-with-user-evolving"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2203.16942.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/sequential-recommendation-with-user-evolving">Sequential Recommendation with User Evolving Preference Decomposition</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/sequential-recommendation-with-user-evolving#code">no code implementations</a> • <span class="author-name-text item-date-pub">31 Mar 2022</span> • <span class="author-span "> <a href="/author/weiqi-shao">Weiqi Shao</a></span>, <span class="author-span "> <a href="/author/xu-chen">Xu Chen</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To solve this problem, in this paper, we propose a novel sequential recommender model via decomposing and modeling user independent preferences.</p> <div class="sota"> </div> <p> <a href="/task/sequential-recommendation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Sequential Recommendation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/sequential-recommendation-with-user-evolving" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/sequential-recommendation-with-user-evolving#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/contrastive-meta-learning-with-behavior"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2202.08523.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/contrastive-meta-learning-with-behavior">Contrastive Meta Learning with Behavior Multiplicity for Recommendation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/contrastive-meta-learning-with-behavior#code">1 code implementation</a> • <span class="author-name-text item-date-pub">17 Feb 2022</span> • <span class="author-span "> <a href="/author/wei-wei">Wei Wei</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span>, <span class="author-span "> <a href="/author/lianghao-xia">Lianghao Xia</a></span>, <span class="author-span "> <a href="/author/yong-xu">Yong Xu</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users.</p> <div class="sota"> </div> <p> <a href="/task/contrastive-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/a2dca842-c6b6-4209-b2a8-dffeac2ef283.jpg"> <span>Contrastive Learning</span> </span> </a> <a href="/task/meta-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001088-6b0b3a7f_0bh9941.jpg"> <span>Meta-Learning</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 48</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/contrastive-meta-learning-with-behavior" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/contrastive-meta-learning-with-behavior#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/learning-reinforced-dynamic-representations"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2112.02787.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/learning-reinforced-dynamic-representations">Gumble Softmax For User Behavior Modeling</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/learning-reinforced-dynamic-representations#code">no code implementations</a> • <span class="author-name-text item-date-pub">6 Dec 2021</span> • <span class="author-span "> <a href="/author/weiqi-shao">Weiqi Shao</a></span>, <span class="author-span "> <a href="/author/xu-chen">Xu Chen</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">We propose a sequential model with dynamic number of representations for recommendation systems (RDRSR).</p> <div class="sota"> </div> <p> <a href="/task/sequential-recommendation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Sequential Recommendation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/learning-reinforced-dynamic-representations" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/learning-reinforced-dynamic-representations#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/sequential-recommendation-with-adaptive"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2112.02812.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/sequential-recommendation-with-adaptive">User behavior understanding in real world settings</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/sequential-recommendation-with-adaptive#code">no code implementations</a> • <span class="author-name-text item-date-pub">6 Dec 2021</span> • <span class="author-span "> <a href="/author/weiqi-shao">Weiqi Shao</a></span>, <span class="author-span "> <a href="/author/xu-chen">Xu Chen</a></span>, <span class="author-span "> <a href="/author/jiashu-zhao">Jiashu Zhao</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">It is necessary to learn a dynamic group of representations according the item groups in a user historical behavior.</p> <div class="sota"> </div> <p> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/sequential-recommendation-with-adaptive" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/sequential-recommendation-with-adaptive#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/global-context-enhanced-social-recommendation"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2110.04039.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/global-context-enhanced-social-recommendation">Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/global-context-enhanced-social-recommendation#code">1 code implementation</a> • <span class="author-name-text item-date-pub">8 Oct 2021</span> • <span class="author-span "> <a href="/author/huance-xu">Huance Xu</a></span>, <span class="author-span "> <a href="/author/chao-huang">Chao Huang</a></span>, <span class="author-span "> <a href="/author/yong-xu">Yong Xu</a></span>, <span class="author-span "> <a href="/author/lianghao-xia">Lianghao Xia</a></span>, <span class="author-span "> <a href="/author/hao-xing">Hao Xing</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.</p> <div class="sota"> </div> <p> <a href="/task/graph-neural-network"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Neural Network</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 14</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/global-context-enhanced-social-recommendation" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/global-context-enhanced-social-recommendation#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/exposing-length-divergence-bias-of-textual"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2109.02431.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/exposing-length-divergence-bias-of-textual">On Length Divergence Bias in Textual Matching Models</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/exposing-length-divergence-bias-of-textual#code">no code implementations</a> • <span class="item-conference-link"> <a href="/conference/findings-acl-2022-5"> Findings (ACL) 2022 </a> </span> • <span class="author-span "> <a href="/author/lan-jiang">Lan Jiang</a></span>, <span class="author-span "> <a href="/author/tianshu-lyu">Tianshu Lyu</a></span>, <span class="author-span "> <a href="/author/yankai-lin">Yankai Lin</a></span>, <span class="author-span "> <a href="/author/meng-chong">Meng Chong</a></span>, <span class="author-span "> <a href="/author/xiaoyong-lyu">Xiaoyong Lyu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To determine whether TM models have adopted such heuristic, we introduce an adversarial evaluation scheme which invalidates the heuristic.</p> <div class="sota"> </div> <p> <a href="/task/semantic-similarity"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Semantic Similarity</span> </span> </a> <a href="/task/semantic-textual-similarity"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000398-44035c5e.jpg"> <span>Semantic Textual Similarity</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/exposing-length-divergence-bias-of-textual" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/exposing-length-divergence-bias-of-textual#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/enhancing-question-generation-with"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2106.10454.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/enhancing-question-generation-with">Enhancing Question Generation with Commonsense Knowledge</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/enhancing-question-generation-with#code">no code implementations</a> • <span class="item-conference-link"> <a href="/conference/ccl-2021-8"> CCL 2021 </a> </span> • <span class="author-span "> <a href="/author/xin-jia">Xin Jia</a></span>, <span class="author-span "> <a href="/author/hao-wang">Hao Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/yunfang-wu">Yunfang Wu</a></span> </p> <p class="item-strip-abstract">Question generation (QG) is to generate natural and grammatical questions that can be answered by a specific answer for a given context.</p> <div class="sota"> </div> <p> <a href="/task/multi-task-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000069-a2f3e151.jpg"> <span>Multi-Task Learning</span> </span> </a> <a href="/task/question-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Question Generation</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/enhancing-question-generation-with#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/enhancing-question-generation-with" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/enhancing-question-generation-with#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/pre-trained-language-model-for-web-scale"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2106.03373.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/pre-trained-language-model-for-web-scale">Pre-trained Language Model for Web-scale Retrieval in Baidu Search</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/pre-trained-language-model-for-web-scale#code">no code implementations</a> • <span class="author-name-text item-date-pub">7 Jun 2021</span> • <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/guan-huang">Guan Huang</a></span>, <span class="author-span "> <a href="/author/jiaxiang-liu">Jiaxiang Liu</a></span>, <span class="author-span "> <a href="/author/weixue-lu">Weixue Lu</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/yukun-li">Yukun Li</a></span>, <span class="author-span "> <a href="/author/daiting-shi">Daiting Shi</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">More importantly, we present a practical system workflow for deploying the model in web-scale retrieval.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/pre-trained-language-model-for-web-scale" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/pre-trained-language-model-for-web-scale#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/enhanced-doubly-robust-learning-for-debiasing"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2105.13623.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/enhanced-doubly-robust-learning-for-debiasing">Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/enhanced-doubly-robust-learning-for-debiasing#code">1 code implementation</a> • <span class="author-name-text item-date-pub">28 May 2021</span> • <span class="author-span "> <a href="/author/siyuan-guo">Siyuan Guo</a></span>, <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/yiding-liu">Yiding Liu</a></span>, <span class="author-span "> <a href="/author/wenwen-ye">Wenwen Ye</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/hechang-chen">Hechang Chen</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span> </p> <p class="item-strip-abstract">Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.</p> <div class="sota"> </div> <p> <a href="/task/counterfactual"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>counterfactual</span> </span> </a> <a href="/task/imputation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001619-246bf592.jpg"> <span>Imputation</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/enhanced-doubly-robust-learning-for-debiasing#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 24</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/enhanced-doubly-robust-learning-for-debiasing" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/enhanced-doubly-robust-learning-for-debiasing#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/pre-trained-language-model-based-ranking-in"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2105.11108.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/pre-trained-language-model-based-ranking-in">Pre-trained Language Model based Ranking in Baidu Search</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/pre-trained-language-model-based-ranking-in#code">no code implementations</a> • <span class="author-name-text item-date-pub">24 May 2021</span> • <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/shengqiang-zhang">Shengqiang Zhang</a></span>, <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/dehong-ma">Dehong Ma</a></span>, <span class="author-span "> <a href="/author/suqi-cheng">Suqi Cheng</a></span>, <span class="author-span "> <a href="/author/daiting-shi">Daiting Shi</a></span>, <span class="author-span "> <a href="/author/zhifan-zhu">Zhifan Zhu</a></span>, <span class="author-span "> <a href="/author/weiyue-su">Weiyue Su</a></span>, <span class="author-span "> <a href="/author/shuaiqiang-wang">Shuaiqiang Wang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">However, it is nontrivial to directly apply these PLM-based rankers to the large-scale web search system due to the following challenging issues:(1) the prohibitively expensive computations of massive neural PLMs, especially for long texts in the web-document, prohibit their deployments in an online ranking system that demands extremely low latency;(2) the discrepancy between existing ranking-agnostic pre-training objectives and the ad-hoc retrieval scenarios that demand comprehensive relevance modeling is another main barrier for improving the online ranking system;(3) a real-world search engine typically involves a committee of ranking components, and thus the compatibility of the individually fine-tuned ranking model is critical for a cooperative ranking system.</p> <div class="sota"> </div> <p> <a href="/task/language-modelling"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000267-8df06634.jpg"> <span>Language Modelling</span> </span> </a> <a href="/task/retrieval"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/8576b666-5d7a-4b88-a1e2-5dcc3ea02f16.jpg"> <span>Retrieval</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/pre-trained-language-model-based-ranking-in" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/pre-trained-language-model-based-ranking-in#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/data-efficient-reinforcement-learning-for-1"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2105.01620.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/data-efficient-reinforcement-learning-for-1">Data-Efficient Reinforcement Learning for Malaria Control</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/data-efficient-reinforcement-learning-for-1#code">no code implementations</a> • <span class="author-name-text item-date-pub">4 May 2021</span> • <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/linfang-hou">Linfang Hou</a></span>, <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials.</p> <div class="sota"> </div> <p> <a href="/task/decision-making"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Decision Making</span> </span> </a> <a href="/task/model-based-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Model-based Reinforcement Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/data-efficient-reinforcement-learning-for-1#tasks"> <span class="badge badge-primary"> <b>+4</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/data-efficient-reinforcement-learning-for-1" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/data-efficient-reinforcement-learning-for-1#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/first-target-and-opinion-then-polarity"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2102.08549.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/first-target-and-opinion-then-polarity">First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/first-target-and-opinion-then-polarity#code">no code implementations</a> • <span class="author-name-text item-date-pub">17 Feb 2021</span> • <span class="author-span "> <a href="/author/lianzhe-huang">Lianzhe Huang</a></span>, <span class="author-span "> <a href="/author/peiyi-wang">Peiyi Wang</a></span>, <span class="author-span "> <a href="/author/sujian-li">Sujian Li</a></span>, <span class="author-span "> <a href="/author/tianyu-liu">Tianyu Liu</a></span>, <span class="author-span "> <a href="/author/xiaodong-zhang">Xiaodong Zhang</a></span>, <span class="author-span "> <a href="/author/zhicong-cheng">Zhicong Cheng</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/houfeng-wang">Houfeng Wang</a></span> </p> <p class="item-strip-abstract">Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities.</p> <div class="sota"> <p> <a href="/sota/aspect-sentiment-triplet-extraction-on-aste"> <img style="height:20px;width:35px;position:relative;top:1px;" src="https://production-media.paperswithcode.com/sota-thumbs/aspect-sentiment-triplet-extraction-on-aste-small_aee8fdc8.png"/> </a> Ranked #9 on <a href="/sota/aspect-sentiment-triplet-extraction-on-aste"> Aspect Sentiment Triplet Extraction on ASTE-Data-V2 </a> </p> </div> <p> <a href="/task/aspect-sentiment-triplet-extraction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000002224-c193a235.jpg"> <span>Aspect Sentiment Triplet Extraction</span> </span> </a> <a href="/task/sentence"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Sentence</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/first-target-and-opinion-then-polarity#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/first-target-and-opinion-then-polarity" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/first-target-and-opinion-then-polarity#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/user-inspired-posterior-network-for"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2102.07919.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/user-inspired-posterior-network-for">User-Inspired Posterior Network for Recommendation Reason Generation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/user-inspired-posterior-network-for#code">no code implementations</a> • <span class="author-name-text item-date-pub">16 Feb 2021</span> • <span class="author-span "> <a href="/author/haolan-zhan">Haolan Zhan</a></span>, <span class="author-span "> <a href="/author/hainan-zhang">Hainan Zhang</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/lei-shen">Lei Shen</a></span>, <span class="author-span "> <a href="/author/yanyan-lan">Yanyan Lan</a></span>, <span class="author-span "> <a href="/author/zhuoye-ding">Zhuoye Ding</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">A simple and effective way is to extract keywords directly from the knowledge-base of products, i. e., attributes or title, as the recommendation reason.</p> <div class="sota"> </div> <p> <a href="/task/question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/56ae901a-265f-415f-b175-ce54133d648b.jpg"> <span>Question Answering</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/user-inspired-posterior-network-for" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/user-inspired-posterior-network-for#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/scenerec-scene-based-graph-neural-networks"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2102.06401.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/scenerec-scene-based-graph-neural-networks">SceneRec: Scene-Based Graph Neural Networks for Recommender Systems</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/scenerec-scene-based-graph-neural-networks#code">no code implementations</a> • <span class="author-name-text item-date-pub">12 Feb 2021</span> • <span class="author-span "> <a href="/author/gang-wang">Gang Wang</a></span>, <span class="author-span "> <a href="/author/ziyi-guo">Ziyi Guo</a></span>, <span class="author-span "> <a href="/author/xiang-li">Xiang Li</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/shuai-ma">Shuai Ma</a></span> </p> <p class="item-strip-abstract">Collaborative filtering has been largely used to advance modern recommender systems to predict user preference.</p> <div class="sota"> </div> <p> <a href="/task/collaborative-filtering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000595-96a2d3eb.jpg"> <span>Collaborative Filtering</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/scenerec-scene-based-graph-neural-networks#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/scenerec-scene-based-graph-neural-networks" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/scenerec-scene-based-graph-neural-networks#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/modeling-topical-relevance-for-multi-turn"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2009.12735.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/modeling-topical-relevance-for-multi-turn">Modeling Topical Relevance for Multi-Turn Dialogue Generation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/modeling-topical-relevance-for-multi-turn#code">no code implementations</a> • <span class="author-name-text item-date-pub">27 Sep 2020</span> • <span class="author-span "> <a href="/author/hainan-zhang">Hainan Zhang</a></span>, <span class="author-span "> <a href="/author/yanyan-lan">Yanyan Lan</a></span>, <span class="author-span "> <a href="/author/liang-pang">Liang Pang</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/zhuoye-ding">Zhuoye Ding</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect the relevant context, and produce appropriate responses accordingly.</p> <div class="sota"> </div> <p> <a href="/task/dialogue-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/ad745387-1dbb-4f42-a482-5ebd1f80e5ac.jpg"> <span>Dialogue Generation</span> </span> </a> <a href="/task/sentence"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Sentence</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/modeling-topical-relevance-for-multi-turn" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/modeling-topical-relevance-for-multi-turn#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/neural-interactive-collaborative-filtering"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2007.02095.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/neural-interactive-collaborative-filtering">Neural Interactive Collaborative Filtering</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/neural-interactive-collaborative-filtering#code">1 code implementation</a> • <span class="author-name-text item-date-pub">4 Jul 2020</span> • <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/yulong-gu">Yulong Gu</a></span>, <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/weidong-liu">Weidong Liu</a></span>, <span class="author-span "> <a href="/author/jimmy-xiangji-huang">Jimmy Xiangji Huang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.</p> <div class="sota"> </div> <p> <a href="/task/collaborative-filtering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000595-96a2d3eb.jpg"> <span>Collaborative Filtering</span> </span> </a> <a href="/task/meta-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001088-6b0b3a7f_0bh9941.jpg"> <span>Meta-Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/neural-interactive-collaborative-filtering#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 20</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/neural-interactive-collaborative-filtering" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/neural-interactive-collaborative-filtering#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/cast-a-correlation-based-adaptive-spectral"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2006.04435.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/cast-a-correlation-based-adaptive-spectral">CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale Data</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/cast-a-correlation-based-adaptive-spectral#code">1 code implementation</a> • <span class="author-name-text item-date-pub">8 Jun 2020</span> • <span class="author-span "> <a href="/author/xiang-li">Xiang Li</a></span>, <span class="author-span "> <a href="/author/ben-kao">Ben Kao</a></span>, <span class="author-span "> <a href="/author/caihua-shan">Caihua Shan</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/martin-ester">Martin Ester</a></span> </p> <p class="item-strip-abstract">We study the problem of applying spectral clustering to cluster multi-scale data, which is data whose clusters are of various sizes and densities.</p> <div class="sota"> </div> <p> <a href="/task/clustering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001594-3ce5d6d8.jpg"> <span>Clustering</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 0</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/cast-a-correlation-based-adaptive-spectral" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/cast-a-correlation-based-adaptive-spectral#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/robust-reinforcement-learning-with-1"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2006.00945.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/robust-reinforcement-learning-with-1">Robust Reinforcement Learning with Wasserstein Constraint</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/robust-reinforcement-learning-with-1#code">no code implementations</a> • <span class="author-name-text item-date-pub">1 Jun 2020</span> • <span class="author-span "> <a href="/author/linfang-hou">Linfang Hou</a></span>, <span class="author-span "> <a href="/author/liang-pang">Liang Pang</a></span>, <span class="author-span "> <a href="/author/xin-hong">Xin Hong</a></span>, <span class="author-span "> <a href="/author/yanyan-lan">Yanyan Lan</a></span>, <span class="author-span "> <a href="/author/zhi-ming-ma">Zhi-Ming Ma</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Robust Reinforcement Learning aims to find the optimal policy with some extent of robustness to environmental dynamics.</p> <div class="sota"> </div> <p> <a href="/task/reinforcement-learning-2"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>reinforcement-learning</span> </span> </a> <a href="/task/reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Reinforcement Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/robust-reinforcement-learning-with-1#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/robust-reinforcement-learning-with-1" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/robust-reinforcement-learning-with-1#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/data-manipulation-towards-effective-instance"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2004.02594.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/data-manipulation-towards-effective-instance">Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/data-manipulation-towards-effective-instance#code">no code implementations</a> • <span class="item-conference-link"> <a href="/conference/acl-2020-6"> ACL 2020 </a> </span> • <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/yonghao-song">Yonghao Song</a></span>, <span class="author-span "> <a href="/author/cheng-zhang">Cheng Zhang</a></span>, <span class="author-span "> <a href="/author/xiaofang-zhao">Xiaofang Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.</p> <div class="sota"> </div> <p> <a href="/task/dialogue-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/ad745387-1dbb-4f42-a482-5ebd1f80e5ac.jpg"> <span>Dialogue Generation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/data-manipulation-towards-effective-instance" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/data-manipulation-towards-effective-instance#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/posterior-gan-towards-informative-and"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2003.02020.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/posterior-gan-towards-informative-and">Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/posterior-gan-towards-informative-and#code">no code implementations</a> • <span class="author-name-text item-date-pub">4 Mar 2020</span> • <span class="author-span "> <a href="/author/shaoxiong-feng">Shaoxiong Feng</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/kan-li">Kan Li</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Neural conversational models learn to generate responses by taking into account the dialog history.</p> <div class="sota"> </div> <p> <a href="/task/decoder"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Decoder</span> </span> </a> <a href="/task/generative-adversarial-network"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Generative Adversarial Network</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/posterior-gan-towards-informative-and#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/posterior-gan-towards-informative-and" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/posterior-gan-towards-informative-and#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/learning-from-easy-to-complex-adaptive-multi"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/pgr-0001162739-02e9a4fa.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/learning-from-easy-to-complex-adaptive-multi">Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/learning-from-easy-to-complex-adaptive-multi#code">1 code implementation</a> • <span class="author-name-text item-date-pub">2 Mar 2020</span> • <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/cheng-zhang">Cheng Zhang</a></span>, <span class="author-span "> <a href="/author/yonghao-song">Yonghao Song</a></span>, <span class="author-span "> <a href="/author/xiaofang-zhao">Xiaofang Zhao</a></span>, <span class="author-span "> <a href="/author/yangxi-li">Yangxi Li</a></span>, <span class="author-span "> <a href="/author/dongsheng-duan">Dongsheng Duan</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses.</p> <div class="sota"> </div> <p> <a href="/task/dialogue-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/ad745387-1dbb-4f42-a482-5ebd1f80e5ac.jpg"> <span>Dialogue Generation</span> </span> </a> <a href="/task/reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Reinforcement Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/learning-from-easy-to-complex-adaptive-multi#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 19</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/learning-from-easy-to-complex-adaptive-multi" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/learning-from-easy-to-complex-adaptive-multi#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/adaptive-parameterization-for-neural-dialogue-1"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2001.06626.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/adaptive-parameterization-for-neural-dialogue-1">Adaptive Parameterization for Neural Dialogue Generation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/adaptive-parameterization-for-neural-dialogue-1#code">1 code implementation</a> • <span class="item-conference-link"> <a href="/conference/ijcnlp-2019-11"> IJCNLP 2019 </a> </span> • <span class="author-span "> <a href="/author/hengyi-cai">Hengyi Cai</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/cheng-zhang">Cheng Zhang</a></span>, <span class="author-span "> <a href="/author/yonghao-song">Yonghao Song</a></span>, <span class="author-span "> <a href="/author/xiaofang-zhao">Xiaofang Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">For each conversation, the model generates parameters of the encoder-decoder by referring to the input context.</p> <div class="sota"> </div> <p> <a href="/task/decoder"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Decoder</span> </span> </a> <a href="/task/dialogue-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/ad745387-1dbb-4f42-a482-5ebd1f80e5ac.jpg"> <span>Dialogue Generation</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 9</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/adaptive-parameterization-for-neural-dialogue-1" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/adaptive-parameterization-for-neural-dialogue-1#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/graphlime-local-interpretable-model"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/2001.06216.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/graphlime-local-interpretable-model">GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/graphlime-local-interpretable-model#code">2 code implementations</a> • <span class="author-name-text item-date-pub">17 Jan 2020</span> • <span class="author-span "> <a href="/author/qiang-huang">Qiang Huang</a></span>, <span class="author-span "> <a href="/author/makoto-yamada">Makoto Yamada</a></span>, <span class="author-span "> <a href="/author/yuan-tian">Yuan Tian</a></span>, <span class="author-span "> <a href="/author/dinesh-singh">Dinesh Singh</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span> </p> <p class="item-strip-abstract">In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method.</p> <div class="sota"> </div> <p> <a href="/task/descriptive"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Descriptive</span> </span> </a> <a href="/task/feature-selection"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>feature selection</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 175</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graphlime-local-interpretable-model" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/graphlime-local-interpretable-model#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/attribute-aware-sequence-network-for-review"> <div class="item-image" style="background-image: url('data:image/jpeg;base64,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');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/attribute-aware-sequence-network-for-review">Attribute-aware Sequence Network for Review Summarization</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/attribute-aware-sequence-network-for-review#code">no code implementations</a> • <span class="item-conference-link"> <a href="/conference/ijcnlp-2019-11"> IJCNLP 2019 </a> </span> • <span class="author-span "> <a href="/author/junjie-li">Junjie Li</a></span>, <span class="author-span "> <a href="/author/xuepeng-wang">Xuepeng Wang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/cheng-qing-zong">Cheng-qing Zong</a></span> </p> <p class="item-strip-abstract">Review summarization aims to generate a condensed summary for a review or multiple reviews.</p> <div class="sota"> </div> <p> <a href="/task/attribute"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Attribute</span> </span> </a> <a href="/task/decoder"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Decoder</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/attribute-aware-sequence-network-for-review" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/attribute-aware-sequence-network-for-review#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/off-policy-learning-for-multiple-loggers"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1907.09652.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/off-policy-learning-for-multiple-loggers">Off-policy Learning for Multiple Loggers</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/off-policy-learning-for-multiple-loggers#code">no code implementations</a> • <span class="author-name-text item-date-pub">23 Jul 2019</span> • <span class="author-span "> <a href="/author/li-he">Li He</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/wei-zeng">Wei Zeng</a></span>, <span class="author-span "> <a href="/author/zhi-ming-ma">Zhi-Ming Ma</a></span>, <span class="author-span "> <a href="/author/yihong-zhao">Yihong Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">To make full use of such historical data, learning policies from multiple loggers becomes necessary.</p> <div class="sota"> </div> <p> <a href="/task/counterfactual"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>counterfactual</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/off-policy-learning-for-multiple-loggers" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/off-policy-learning-for-multiple-loggers#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/deep-social-collaborative-filtering"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1907.06853.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/deep-social-collaborative-filtering">Deep Social Collaborative Filtering</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/deep-social-collaborative-filtering#code">no code implementations</a> • <span class="author-name-text item-date-pub">16 Jul 2019</span> • <span class="author-span "> <a href="/author/wenqi-fan">Wenqi Fan</a></span>, <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jian-ping-wang">Jian-Ping Wang</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span "> <a href="/author/qing-li">Qing Li</a></span> </p> <p class="item-strip-abstract">Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.</p> <div class="sota"> </div> <p> <a href="/task/collaborative-filtering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000595-96a2d3eb.jpg"> <span>Collaborative Filtering</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/deep-social-collaborative-filtering" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/deep-social-collaborative-filtering#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/toward-simulating-environments-in"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1906.11462.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/toward-simulating-environments-in">Toward Simulating Environments in Reinforcement Learning Based Recommendations</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/toward-simulating-environments-in#code">no code implementations</a> • <span class="author-name-text item-date-pub">27 Jun 2019</span> • <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.</p> <div class="sota"> </div> <p> <a href="/task/generative-adversarial-network"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Generative Adversarial Network</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/toward-simulating-environments-in#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/toward-simulating-environments-in" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/toward-simulating-environments-in#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/graph-neural-networks-for-social"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/papergithubrepo/pgr-0000903693-e77364d2.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/graph-neural-networks-for-social">Graph Neural Networks for Social Recommendation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/graph-neural-networks-for-social#code">8 code implementations</a> • <span class="author-name-text item-date-pub">19 Feb 2019</span> • <span class="author-span "> <a href="/author/wenqi-fan">Wenqi Fan</a></span>, <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span "> <a href="/author/qing-li">Qing Li</a></span>, <span class="author-span "> <a href="/author/yuan-he">Yuan He</a></span>, <span class="author-span "> <a href="/author/eric-zhao">Eric Zhao</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.</p> <div class="sota"> <p> <a href="/sota/recommendation-systems-on-epinions"> <img style="height:20px;width:35px;position:relative;top:1px;" src="https://production-media.paperswithcode.com/sota-thumbs/recommendation-systems-on-epinions-small_d7aac2c4.png"/> </a> Ranked #3 on <a href="/sota/recommendation-systems-on-epinions"> Recommendation Systems on Epinions </a> (using extra training data) </p> </div> <p> <a href="/task/graph-neural-network"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Graph Neural Network</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 364</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/graph-neural-networks-for-social" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/graph-neural-networks-for-social#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/reinforcement-learning-to-optimize-long-term"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1902.05570.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/reinforcement-learning-to-optimize-long-term">Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/reinforcement-learning-to-optimize-long-term#code">no code implementations</a> • <span class="author-name-text item-date-pub">13 Feb 2019</span> • <span class="author-span "> <a href="/author/lixin-zou">Lixin Zou</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/zhuoye-ding">Zhuoye Ding</a></span>, <span class="author-span "> <a href="/author/jiaxing-song">Jiaxing Song</a></span>, <span class="author-span "> <a href="/author/weidong-liu">Weidong Liu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Though reinforcement learning~(RL) naturally fits the problem of maximizing the long term rewards, applying RL to optimize long-term user engagement is still facing challenges: user behaviors are versatile and difficult to model, which typically consists of both instant feedback~(e. g. clicks, ordering) and delayed feedback~(e. g. dwell time, revisit); in addition, performing effective off-policy learning is still immature, especially when combining bootstrapping and function approximation.</p> <div class="sota"> </div> <p> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a href="/task/reinforcement-learning-2"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>reinforcement-learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/reinforcement-learning-to-optimize-long-term#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/reinforcement-learning-to-optimize-long-term" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/reinforcement-learning-to-optimize-long-term#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/model-based-reinforcement-learning-for-whole"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1902.03987.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/model-based-reinforcement-learning-for-whole">Whole-Chain Recommendations</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/model-based-reinforcement-learning-for-whole#code">no code implementations</a> • <span class="author-name-text item-date-pub">11 Feb 2019</span> • <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/linxin-zou">Linxin Zou</a></span>, <span class="author-span "> <a href="/author/hui-liu">Hui Liu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.</p> <div class="sota"> </div> <p> <a href="/task/multi-agent-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Multi-agent Reinforcement Learning</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/model-based-reinforcement-learning-for-whole#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/model-based-reinforcement-learning-for-whole" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/model-based-reinforcement-learning-for-whole#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/product-aware-answer-generation-in-e-commerce"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1901.07696.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/product-aware-answer-generation-in-e-commerce">Product-Aware Answer Generation in E-Commerce Question-Answering</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/product-aware-answer-generation-in-e-commerce#code">1 code implementation</a> • <span class="author-name-text item-date-pub">23 Jan 2019</span> • <span class="author-span "> <a href="/author/shen-gao">Shen Gao</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span "> <a href="/author/yihong-eric-zhao">Yihong Eric Zhao</a></span>, <span class="author-span "> <a href="/author/dongyan-zhao">Dongyan Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/rui-yan">Rui Yan</a></span> </p> <p class="item-strip-abstract">In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.</p> <div class="sota"> <p> <a href="/sota/product-question-answering-on-jd-product"> <img style="height:20px;width:35px;position:relative;top:1px;" src="https://production-media.paperswithcode.com/sota-thumbs/product-question-answering-on-jd-product-small_1677e90b.png"/> </a>  Ranked #1 on <a class="sota-task" href="/sota/product-question-answering-on-jd-product"> Question Answering on JD Product Question Answer </a> </p> </div> <p> <a href="/task/answer-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Answer Generation</span> </span> </a> <a href="/task/question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/56ae901a-265f-415f-b175-ce54133d648b.jpg"> <span>Question Answering</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 37</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/product-aware-answer-generation-in-e-commerce" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/product-aware-answer-generation-in-e-commerce#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/deep-reinforcement-learning-for-search"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1812.07127.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/deep-reinforcement-learning-for-search">Deep reinforcement learning for search, recommendation, and online advertising: a survey</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/deep-reinforcement-learning-for-search#code">no code implementations</a> • <span class="author-name-text item-date-pub">18 Dec 2018</span> • <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.</p> <div class="sota"> </div> <p> <a href="/task/deep-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Deep Reinforcement Learning</span> </span> </a> <a href="/task/reinforcement-learning-2"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>reinforcement-learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/deep-reinforcement-learning-for-search#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/deep-reinforcement-learning-for-search" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/deep-reinforcement-learning-for-search#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/streaming-graph-neural-networks"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1810.10627.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/streaming-graph-neural-networks">Streaming Graph Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/streaming-graph-neural-networks#code">2 code implementations</a> • <span class="author-name-text item-date-pub">24 Oct 2018</span> • <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span "> <a href="/author/ziyi-guo">Ziyi Guo</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span "> <a href="/author/eric-zhao">Eric Zhao</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Current graph neural network models cannot utilize the dynamic information in dynamic graphs.</p> <div class="sota"> </div> <p> <a href="/task/community-detection"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000600-5f543d15_cW4Y1MK.jpg"> <span>Community Detection</span> </span> </a> <a href="/task/classification"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000001592-811f0118_3TU7fCb.jpg"> <span>General Classification</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/streaming-graph-neural-networks#tasks"> <span class="badge badge-primary"> <b>+4</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 51</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/streaming-graph-neural-networks" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/streaming-graph-neural-networks#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/explicit-state-tracking-with-semi-supervision"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1808.10596.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/explicit-state-tracking-with-semi-supervision">Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/explicit-state-tracking-with-semi-supervision#code">2 code implementations</a> • <span class="author-name-text item-date-pub">31 Aug 2018</span> • <span class="author-span "> <a href="/author/xisen-jin">Xisen Jin</a></span>, <span class="author-span "> <a href="/author/wenqiang-lei">Wenqiang Lei</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/shangsong-liang">Shangsong Liang</a></span>, <span class="author-span "> <a href="/author/yihong-zhao">Yihong Zhao</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.</p> <div class="sota"> </div> <p> <a href="/task/decoder"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Decoder</span> </span> </a> <a href="/task/dialogue-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/ad745387-1dbb-4f42-a482-5ebd1f80e5ac.jpg"> <span>Dialogue Generation</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/explicit-state-tracking-with-semi-supervision#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 22</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/explicit-state-tracking-with-semi-supervision" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/explicit-state-tracking-with-semi-supervision#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/linked-recurrent-neural-networks"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1808.06170.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/linked-recurrent-neural-networks">Linked Recurrent Neural Networks</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/linked-recurrent-neural-networks#code">no code implementations</a> • <span class="author-name-text item-date-pub">19 Aug 2018</span> • <span class="author-span "> <a href="/author/zhiwei-wang">Zhiwei Wang</a></span>, <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation.</p> <div class="sota"> </div> <p> <a href="/task/document-classification"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Document Classification</span> </span> </a> <a href="/task/machine-translation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000257-2b560008_M7RFnV9.jpg"> <span>Machine Translation</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/linked-recurrent-neural-networks#tasks"> <span class="badge badge-primary"> <b>+3</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/linked-recurrent-neural-networks" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/linked-recurrent-neural-networks#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/multi-dimensional-graph-convolutional"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1808.06099.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/multi-dimensional-graph-convolutional">Multi-dimensional Graph Convolutional Networks</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/multi-dimensional-graph-convolutional#code">no code implementations</a> • <span class="author-name-text item-date-pub">18 Aug 2018</span> • <span class="author-span "> <a href="/author/yao-ma">Yao Ma</a></span>, <span class="author-span "> <a href="/author/suhang-wang">Suhang Wang</a></span>, <span class="author-span "> <a href="/author/charu-c-aggarwal">Charu C. Aggarwal</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video.</p> <div class="sota"> </div> <p> <span class="badge badge-primary badge-primary-nohover">Social and Information Networks</span> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/multi-dimensional-graph-convolutional" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/multi-dimensional-graph-convolutional#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/sequicity-simplifying-task-oriented-dialogue"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/66872.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/sequicity-simplifying-task-oriented-dialogue">Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/sequicity-simplifying-task-oriented-dialogue#code">1 code implementation</a> • <span class="item-conference-link"> <a href="/conference/acl-2018-7"> ACL 2018 </a> </span> • <span class="author-span "> <a href="/author/wenqiang-lei">Wenqiang Lei</a></span>, <span class="author-span "> <a href="/author/xisen-jin">Xisen Jin</a></span>, <span class="author-span "> <a href="/author/min-yen-kan">Min-Yen Kan</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span "> <a href="/author/xiangnan-he">Xiangnan He</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility.</p> <div class="sota"> </div> <p> <a href="/task/reinforcement-learning-2"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>reinforcement-learning</span> </span> </a> <a href="/task/reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Reinforcement Learning</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/sequicity-simplifying-task-oriented-dialogue#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 155</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/sequicity-simplifying-task-oriented-dialogue" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/sequicity-simplifying-task-oriented-dialogue#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/knowledge-diffusion-for-neural-dialogue"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/66877.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/knowledge-diffusion-for-neural-dialogue">Knowledge Diffusion for Neural Dialogue Generation</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/knowledge-diffusion-for-neural-dialogue#code">1 code implementation</a> • <span class="item-conference-link"> <a href="/conference/acl-2018-7"> ACL 2018 </a> </span> • <span class="author-span "> <a href="/author/shuman-liu">Shuman Liu</a></span>, <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/zhaochun-ren">Zhaochun Ren</a></span>, <span class="author-span "> <a href="/author/yang-feng">Yang Feng</a></span>, <span class="author-span "> <a href="/author/qun-liu">Qun Liu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.</p> <div class="sota"> </div> <p> <a href="/task/dialogue-generation"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/ad745387-1dbb-4f42-a482-5ebd1f80e5ac.jpg"> <span>Dialogue Generation</span> </span> </a> <a href="/task/question-answering"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/56ae901a-265f-415f-b175-ce54133d648b.jpg"> <span>Question Answering</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/knowledge-diffusion-for-neural-dialogue#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 21</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/knowledge-diffusion-for-neural-dialogue" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/knowledge-diffusion-for-neural-dialogue#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/deep-reinforcement-learning-for-page-wise"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1805.02343.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/deep-reinforcement-learning-for-page-wise">Deep Reinforcement Learning for Page-wise Recommendations</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/deep-reinforcement-learning-for-page-wise#code">no code implementations</a> • <span class="author-name-text item-date-pub">7 May 2018</span> • <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/liang-zhang">Liang Zhang</a></span>, <span class="author-span "> <a href="/author/zhuoye-ding">Zhuoye Ding</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.</p> <div class="sota"> </div> <p> <a href="/task/deep-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Deep Reinforcement Learning</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/deep-reinforcement-learning-for-page-wise#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/deep-reinforcement-learning-for-page-wise" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/deep-reinforcement-learning-for-page-wise#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/recommendations-with-negative-feedback-via"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1802.06501.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/recommendations-with-negative-feedback-via">Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/recommendations-with-negative-feedback-via#code">no code implementations</a> • <span class="author-name-text item-date-pub">19 Feb 2018</span> • <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/liang-zhang">Liang Zhang</a></span>, <span class="author-span "> <a href="/author/zhuoye-ding">Zhuoye Ding</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span> </p> <p class="item-strip-abstract">Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.</p> <div class="sota"> </div> <p> <a href="/task/deep-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Deep Reinforcement Learning</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/recommendations-with-negative-feedback-via#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/recommendations-with-negative-feedback-via" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/recommendations-with-negative-feedback-via#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/deep-reinforcement-learning-for-list-wise"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1801.00209.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/deep-reinforcement-learning-for-list-wise">Deep Reinforcement Learning for List-wise Recommendations</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/deep-reinforcement-learning-for-list-wise#code">7 code implementations</a> • <span class="author-name-text item-date-pub">30 Dec 2017</span> • <span class="author-span "> <a href="/author/xiangyu-zhao">Xiangyu Zhao</a></span>, <span class="author-span "> <a href="/author/liang-zhang">Liang Zhang</a></span>, <span class="author-span "> <a href="/author/long-xia">Long Xia</a></span>, <span class="author-span "> <a href="/author/zhuoye-ding">Zhuoye Ding</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.</p> <div class="sota"> </div> <p> <a href="/task/deep-reinforcement-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Deep Reinforcement Learning</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/deep-reinforcement-learning-for-list-wise#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary"><span class=" icon-wrapper icon-ion" data-name="star"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M394 480a16 16 0 0 1-9.39-3L256 383.76 127.39 477a16 16 0 0 1-24.55-18.08L153 310.35 23 221.2a16 16 0 0 1 9-29.2h160.38l48.4-148.95a16 16 0 0 1 30.44 0l48.4 149H480a16 16 0 0 1 9.05 29.2L359 310.35l50.13 148.53A16 16 0 0 1 394 480z"/></svg></span> 179</span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/deep-reinforcement-learning-for-list-wise" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/deep-reinforcement-learning-for-list-wise#code" class="badge badge-dark "> <span class=" icon-wrapper icon-ion" data-name="logo-github"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M256 32C132.3 32 32 134.9 32 261.7c0 101.5 64.2 187.5 153.2 217.9a17.56 17.56 0 0 0 3.8.4c8.3 0 11.5-6.1 11.5-11.4 0-5.5-.2-19.9-.3-39.1a102.4 102.4 0 0 1-22.6 2.7c-43.1 0-52.9-33.5-52.9-33.5-10.2-26.5-24.9-33.6-24.9-33.6-19.5-13.7-.1-14.1 1.4-14.1h.1c22.5 2 34.3 23.8 34.3 23.8 11.2 19.6 26.2 25.1 39.6 25.1a63 63 0 0 0 25.6-6c2-14.8 7.8-24.9 14.2-30.7-49.7-5.8-102-25.5-102-113.5 0-25.1 8.7-45.6 23-61.6-2.3-5.8-10-29.2 2.2-60.8a18.64 18.64 0 0 1 5-.5c8.1 0 26.4 3.1 56.6 24.1a208.21 208.21 0 0 1 112.2 0c30.2-21 48.5-24.1 56.6-24.1a18.64 18.64 0 0 1 5 .5c12.2 31.6 4.5 55 2.2 60.8 14.3 16.1 23 36.6 23 61.6 0 88.2-52.4 107.6-102.3 113.3 8 7.1 15.2 21.1 15.2 42.5 0 30.7-.3 55.5-.3 63 0 5.4 3.1 11.5 11.4 11.5a19.35 19.35 0 0 0 4-.4C415.9 449.2 480 363.1 480 261.7 480 134.9 379.7 32 256 32z"/></svg></span> Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/a-survey-on-dialogue-systems-recent-advances"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1711.01731.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/a-survey-on-dialogue-systems-recent-advances">A Survey on Dialogue Systems: Recent Advances and New Frontiers</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/a-survey-on-dialogue-systems-recent-advances#code">no code implementations</a> • <span class="author-name-text item-date-pub">6 Nov 2017</span> • <span class="author-span "> <a href="/author/hongshen-chen">Hongshen Chen</a></span>, <span class="author-span "> <a href="/author/xiaorui-liu">Xiaorui Liu</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span> </p> <p class="item-strip-abstract">Dialogue systems have attracted more and more attention.</p> <div class="sota"> </div> <p> <a href="/task/deep-learning"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Deep Learning</span> </span> </a> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/a-survey-on-dialogue-systems-recent-advances#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/a-survey-on-dialogue-systems-recent-advances" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/a-survey-on-dialogue-systems-recent-advances#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/ultra-high-dimensional-nonlinear-feature"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1608.04048.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/ultra-high-dimensional-nonlinear-feature">Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/ultra-high-dimensional-nonlinear-feature#code">no code implementations</a> • <span class="author-name-text item-date-pub">14 Aug 2016</span> • <span class="author-span "> <a href="/author/makoto-yamada">Makoto Yamada</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span "> <a href="/author/jose-lugo-martinez">Jose Lugo-Martinez</a></span>, <span class="author-span "> <a href="/author/ermin-hodzic">Ermin Hodzic</a></span>, <span class="author-span "> <a href="/author/raunak-shrestha">Raunak Shrestha</a></span>, <span class="author-span "> <a href="/author/avishek-saha">Avishek Saha</a></span>, <span class="author-span "> <a href="/author/hua-ouyang">Hua Ouyang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/hiroshi-mamitsuka">Hiroshi Mamitsuka</a></span>, <span class="author-span "> <a href="/author/cenk-sahinalp">Cenk Sahinalp</a></span>, <span class="author-span "> <a href="/author/predrag-radivojac">Predrag Radivojac</a></span>, <span class="author-span "> <a href="/author/filippo-menczer">Filippo Menczer</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span> </p> <p class="item-strip-abstract">However, sophisticated learning models are computationally unfeasible for data with millions of features.</p> <div class="sota"> </div> <p> <a href="/task/cloud-computing"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Cloud Computing</span> </span> </a> <a href="/task/dimensionality-reduction"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000831-cdc3967d.jpg"> <span>Dimensionality Reduction</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/ultra-high-dimensional-nonlinear-feature#tasks"> <span class="badge badge-primary"> <b>+2</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/ultra-high-dimensional-nonlinear-feature" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/ultra-high-dimensional-nonlinear-feature#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/streaming-recommender-systems"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1607.06182.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/streaming-recommender-systems">Streaming Recommender Systems</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/streaming-recommender-systems#code">no code implementations</a> • <span class="author-name-text item-date-pub">21 Jul 2016</span> • <span class="author-span "> <a href="/author/shiyu-chang">Shiyu Chang</a></span>, <span class="author-span "> <a href="/author/yang-zhang">Yang Zhang</a></span>, <span class="author-span "> <a href="/author/jiliang-tang">Jiliang Tang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span>, <span class="author-span "> <a href="/author/mark-a-hasegawa-johnson-1">Mark A. Hasegawa-Johnson</a></span>, <span class="author-span "> <a href="/author/thomas-s-huang">Thomas S. Huang</a></span> </p> <p class="item-strip-abstract">The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios.</p> <div class="sota"> </div> <p> <a href="/task/recommendation-systems"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/thumbnails/task/task-0000000592-2508deea.jpg"> <span>Recommendation Systems</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/streaming-recommender-systems" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/streaming-recommender-systems#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/consistent-collective-matrix-completion-under"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1412.2113.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/consistent-collective-matrix-completion-under">Consistent Collective Matrix Completion under Joint Low Rank Structure</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/consistent-collective-matrix-completion-under#code">no code implementations</a> • <span class="author-name-text item-date-pub">5 Dec 2014</span> • <span class="author-span "> <a href="/author/suriya-gunasekar">Suriya Gunasekar</a></span>, <span class="author-span "> <a href="/author/makoto-yamada">Makoto Yamada</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span> </p> <p class="item-strip-abstract">We address the collective matrix completion problem of jointly recovering a collection of matrices with shared structure from partial (and potentially noisy) observations.</p> <div class="sota"> </div> <p> <a href="/task/matrix-completion"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Matrix Completion</span> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/consistent-collective-matrix-completion-under" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/consistent-collective-matrix-completion-under#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> <div class="row infinite-item item paper-card">  <div class="col-lg-3 item-image-col"> <a href="/paper/n3lars-minimum-redundancy-maximum-relevance"> <div class="item-image" style="background-image: url('https://production-media.paperswithcode.com/thumbnails/paper/1411.2331.jpg');"> </div> </a> </div> <div class="col-lg-9 item-col"> <div class="row"> <div class="col-lg-9 item-content"> <h1><a href="/paper/n3lars-minimum-redundancy-maximum-relevance">N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data</a></h1> <p class="author-section" style="padding-top:2px"> <a href="/paper/n3lars-minimum-redundancy-maximum-relevance#code">no code implementations</a> • <span class="author-name-text item-date-pub">10 Nov 2014</span> • <span class="author-span "> <a href="/author/makoto-yamada">Makoto Yamada</a></span>, <span class="author-span "> <a href="/author/avishek-saha">Avishek Saha</a></span>, <span class="author-span "> <a href="/author/hua-ouyang">Hua Ouyang</a></span>, <span class="author-span author-matched"> <a href="/author/dawei-yin">Dawei Yin</a></span>, <span class="author-span "> <a href="/author/yi-chang">Yi Chang</a></span> </p> <p class="item-strip-abstract">We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way.</p> <div class="sota"> </div> <p> <a href="/task/distributed-computing"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>Distributed Computing</span> </span> </a> <a href="/task/feature-selection"> <span class="badge badge-primary"> <img src="https://production-media.paperswithcode.com/tasks/default.gif"> <span>feature selection</span> </span> </a> <a style="position: relative; top: -2px;" href="/paper/n3lars-minimum-redundancy-maximum-relevance#tasks"> <span class="badge badge-primary"> <b>+1</b> </span> </a> </p> </div> <div class="col-lg-3 item-interact text-center"> <div class="entity-stars"> <span class="badge badge-secondary" style="border:none;background-color:transparent"> </span> </div> <div class="entity" style="margin-bottom: 20px;"> <a href="/paper/n3lars-minimum-redundancy-maximum-relevance" class="badge badge-light "> <span class=" icon-wrapper icon-ion" data-name="document"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M428 224H288a48 48 0 0 1-48-48V36a4 4 0 0 0-4-4h-92a64 64 0 0 0-64 64v320a64 64 0 0 0 64 64h224a64 64 0 0 0 64-64V228a4 4 0 0 0-4-4z"/><path d="M419.22 188.59L275.41 44.78a2 2 0 0 0-3.41 1.41V176a16 16 0 0 0 16 16h129.81a2 2 0 0 0 1.41-3.41z"/></svg></span> Paper </a> <br/> <a href="/paper/n3lars-minimum-redundancy-maximum-relevance#code" class="badge badge-dark badge-nocode "> <span class=" icon-wrapper icon-ion" data-name="add"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path fill="none" stroke="#000" stroke-linecap="round" stroke-linejoin="round" stroke-width="32" d="M256 112v288m144-144H112"/></svg></span> Add Code </a> <br/> </div> </div> </div> </div> </div> </div> <div class="loading" style="display: none;"> <div class="loader-ellips infinite-scroll-request"> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> <span class="loader-ellips__dot"></span> </div> </div> <div class="search-submit-paper text-center" style="font-size:16px;padding-bottom:30px;"> Cannot find the paper you are looking for? You can <a href="/submit-paper">Submit</a> a new open access paper. </div> </div> </div> <div class="footer"> <div class="footer-contact"> <span class="footer-contact-item">Contact us on:</span> <a class="footer-contact-item" href="mailto:hello@paperswithcode.com"> <span class=" icon-wrapper icon-ion" data-name="mail"><svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512"><path d="M424 80H88a56.06 56.06 0 0 0-56 56v240a56.06 56.06 0 0 0 56 56h336a56.06 56.06 0 0 0 56-56V136a56.06 56.06 0 0 0-56-56zm-14.18 92.63l-144 112a16 16 0 0 1-19.64 0l-144-112a16 16 0 1 1 19.64-25.26L256 251.73l134.18-104.36a16 16 0 0 1 19.64 25.26z"/></svg></span> hello@paperswithcode.com </a>. <span class="footer-contact-item"> Papers With Code is a free resource with all data licensed under <a rel="noreferrer" href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA</a>. </span> </div> <div class="footer-links"> <a href="/site/terms">Terms</a> <a href="/site/data-policy">Data policy</a> <a href="/site/cookies-policy">Cookies policy</a> <a href="/about#team" class="fair-logo"> from <img src="data:image/png;base64,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"> </a> </div> </div> <script> run_after_frontend_loaded.push(() => { var infinite = new Waypoint.Infinite({ element: $('.infinite-container')[0], onBeforePageLoad: function () { $('.loading').show(); }, onAfterPageLoad: function ($items) { $('.loading').hide(); } }); }); function toggleTweets(paper_id){ var element_id = "#tweets-" + paper_id; $( element_id ).toggle("slow"); } function fullHeight(paper_id){ var element_id = "#tweets-" + paper_id; // hide read more element var read_more = $("#see-more-"+paper_id); read_more.fadeOut(); $(element_id).get(0).style.setProperty("max-height", "9999px") } </script> <script> run_after_frontend_loaded.push(() => { $(function() { $.fn.modal.Constructor.prototype._enforceFocus = function() {}; $.widget( "custom.catcomplete", $.ui.autocomplete, { _create: function() { this._super(); this.widget().menu( "option", "items", "> :not(.ui-autocomplete-category)" ); }, /** Overrides the _renderItem method in jquery to allow for search result images and icons **/ _renderItem: function( ul, item ) { /** If we have an image in the seearch item then render it; if no task image available, use default **/ if ( "image" in item ) { if ( item.image ) { var image_url = item.image; } else { var image_url = "https://production-media.paperswithcode.com/" + "tasks/default.gif"; } return $( "<li>" ) .append( $( "<div>" ).text( item.label ).prepend( $( "<img src=" + image_url + ">") ) ) .appendTo( ul ); } else { return $( "<li>" ) .append($("<div>").text(item.label)) .appendTo( ul ); } }, _renderMenu: function( ul, items ) { var that = this, currentCategory = ""; $.each( items, function( index, item ) { var li; if ( item.category != currentCategory ) { ul.append( "<li class='ui-autocomplete-category'>" + item.category + "</li>" ); currentCategory = item.category; } li = that._renderItemData( ul, item ); if (item.meta !== null) { li.attr('data-qmeta', item.meta); } if ( item.category ) { li.attr( "aria-label", item.category + " : " + item.label ); } }); } }); $( "#id_global_search_input" ).catcomplete({ minLength: 2, source: function( request, response ) { var term = request.term; $.get( "/api/search-autocomplete/", {"q": term}, function(data){ let t = data.tasks, lb = data.leaderboards, p = data.papers, d = data.datasets, m = data.methods; let ts = [], lbs = [], ps = [], ds = [], ms = []; let total = 0; let maxItems = 12; for (let i=0; i<5 && total < maxItems; i++) { if (t.length && total < maxItems) { ts.push({ label: t[0].name, image: t[0].image, category: "Tasks", meta: null, }); t.shift(); total ++; } if (lb.length && total < maxItems) { lbs.push({ label: lb[0].name, image: lb[0].image, category: "Leaderboards", meta: lb[0].slug }); lb.shift(); total ++; } if (p.length && total < maxItems) { ps.push({label: p[0].title, category: "Papers", meta: null}); p.shift(); total ++; } if (d.length && total < maxItems) { ds.push({ label: d[0].name, image: d[0].image, category: "Datasets", meta: d[0].slug, }); d.shift(); total ++; } if (m.length && total < maxItems) { ms.push({ label: m[0].name, image: m[0].image, category: "Methods", meta: null }); m.shift(); total ++; } } let searchData = ts.concat(lbs, ps, ds, ms); response(searchData); }); }, select: function(event, ui) { $("#id_global_search_input").val(ui.item.label); if (typeof gtag !== 'undefined') { gtag('event', 'SiteActions', { 'event_category': 'Search', 'event_label': ui.item.category, }); } if (ui.item.meta === null) { $('#q_meta').val(''); $('#q_meta').removeAttr('name'); } else { if(!$('#q_meta').attr("name")) { $('#q_meta').attr('name', 'q_meta'); } $('#q_meta').val(ui.item.meta); } $('#q_type').val(ui.item.category.toLowerCase()); $("#id_global_search_form").submit(); } }); if ($(window).width() < 1200 && $(window).width() > 992 ) { $("#id_global_search_input").attr("placeholder", "Search"); } // Setup csrf token for ajax requests let getCookie = (name) => { var cookieValue = null; if (document.cookie && document.cookie !== '') { var cookies = document.cookie.split(';'); for (var i = 0; i < cookies.length; i++) { var cookie = jQuery.trim(cookies[i]); // Does this cookie string begin with the name we want? if (cookie.substring(0, name.length + 1) === (name + '=')) { cookieValue = decodeURIComponent(cookie.substring(name.length + 1)); break; } } } return cookieValue; }; let csrftoken = getCookie('csrftoken'); // Make sure we use the most up-to-date CSRF token $("input[name='csrfmiddlewaretoken']").val(csrftoken); function csrfSafeMethod(method) { // these HTTP methods do not require CSRF protection return (/^(GET|HEAD|OPTIONS|TRACE)$/.test(method)); } $.ajaxSetup({ beforeSend: function(xhr, settings) { if (!csrfSafeMethod(settings.type) && !this.crossDomain) { xhr.setRequestHeader("X-CSRFToken", csrftoken); } } }); }); }); </script> <script>!function(e){function t(t){for(var n,a,s=t[0],u=t[1],f=t[2],i=0,d=[];i<s.length;i++)a=s[i],Object.prototype.hasOwnProperty.call(o,a)&&o[a]&&d.push(o[a][0]),o[a]=0;for(n in u)Object.prototype.hasOwnProperty.call(u,n)&&(e[n]=u[n]);for(l&&l(t);d.length;)d.shift()();return c.push.apply(c,f||[]),r()}function r(){for(var e,t=0;t<c.length;t++){for(var r=c[t],n=!0,a=1;a<r.length;a++){var u=r[a];0!==o[u]&&(n=!1)}n&&(c.splice(t--,1),e=s(s.s=r[0]))}return e}var n={},a={11:0},o={11:0},c=[];function s(t){if(n[t])return n[t].exports;var r=n[t]={i:t,l:!1,exports:{}};return e[t].call(r.exports,r,r.exports,s),r.l=!0,r.exports}s.e=function(e){var t=[];a[e]?t.push(a[e]):0!==a[e]&&{2:1,3:1,5:1,6:1,8:1,9:1,10:1}[e]&&t.push(a[e]=new Promise((function(t,r){for(var n="static/css/"+({4:"chart",5:"conference-page",6:"example-page",8:"newsletters-create-page",9:"newsletters-edit-page",10:"newsletters-list-page",12:"table"}[e]||e)+"."+{0:"31d6cfe0",1:"31d6cfe0",2:"5745a9fd",3:"05600cd7",4:"31d6cfe0",5:"67565070",6:"8444f163",8:"f8a273b3",9:"f8a273b3",10:"db3e0a85",12:"31d6cfe0",14:"31d6cfe0",15:"31d6cfe0"}[e]+".chunk.css",o=s.p+n,c=document.getElementsByTagName("link"),u=0;u<c.length;u++){var f=(l=c[u]).getAttribute("data-href")||l.getAttribute("href");if("stylesheet"===l.rel&&(f===n||f===o))return t()}var i=document.getElementsByTagName("style");for(u=0;u<i.length;u++){var l;if((f=(l=i[u]).getAttribute("data-href"))===n||f===o)return t()}var d=document.createElement("link");d.rel="stylesheet",d.type="text/css",d.onload=t,d.onerror=function(t){var n=t&&t.target&&t.target.src||o,c=new Error("Loading CSS chunk "+e+" failed.\n("+n+")");c.code="CSS_CHUNK_LOAD_FAILED",c.request=n,delete a[e],d.parentNode.removeChild(d),r(c)},d.href=o,document.getElementsByTagName("head")[0].appendChild(d)})).then((function(){a[e]=0})));var r=o[e];if(0!==r)if(r)t.push(r[2]);else{var n=new Promise((function(t,n){r=o[e]=[t,n]}));t.push(r[2]=n);var c,u=document.createElement("script");u.charset="utf-8",u.timeout=120,s.nc&&u.setAttribute("nonce",s.nc),u.src=function(e){return s.p+"static/js/"+({4:"chart",5:"conference-page",6:"example-page",8:"newsletters-create-page",9:"newsletters-edit-page",10:"newsletters-list-page",12:"table"}[e]||e)+"."+{0:"041a0327",1:"eb8f85bf",2:"57df0e43",3:"dd682e9c",4:"934a42ca",5:"ddc33be8",6:"f5234ef0",8:"c76f72bd",9:"aa24afbf",10:"a749f71a",12:"c5756280",14:"be7b1031",15:"b8393014"}[e]+".chunk.js"}(e);var f=new Error;c=function(t){u.onerror=u.onload=null,clearTimeout(i);var r=o[e];if(0!==r){if(r){var n=t&&("load"===t.type?"missing":t.type),a=t&&t.target&&t.target.src;f.message="Loading chunk "+e+" failed.\n("+n+": "+a+")",f.name="ChunkLoadError",f.type=n,f.request=a,r[1](f)}o[e]=void 0}};var i=setTimeout((function(){c({type:"timeout",target:u})}),12e4);u.onerror=u.onload=c,document.head.appendChild(u)}return Promise.all(t)},s.m=e,s.c=n,s.d=function(e,t,r){s.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},s.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},s.t=function(e,t){if(1&t&&(e=s(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var r=Object.create(null);if(s.r(r),Object.defineProperty(r,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var n in e)s.d(r,n,function(t){return e[t]}.bind(null,n));return r},s.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return s.d(t,"a",t),t},s.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},s.p="https://production-assets.paperswithcode.com/",s.oe=function(e){throw console.error(e),e};var u=this.webpackJsonpfrontend=this.webpackJsonpfrontend||[],f=u.push.bind(u);u.push=t,u=u.slice();for(var i=0;i<u.length;i++)t(u[i]);var l=f;r()}([])</script><script src="https://production-assets.paperswithcode.com/static/js/13.aa3fa037.chunk.js"></script><script src="https://production-assets.paperswithcode.com/static/js/main.99ee382b.chunk.js"></script> </body> </html>

CINXE.COM

Dawei Yin | Papers With Code