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My research combines machine learning with traditional literary scholarship, and has focused on metric learning, the adaptation of large language models to historical domains, and analyzing orthographic variation in literary […]" /> <meta property="og:url" content="https://www.clsp.jhu.edu/people/affiliated-faculty/" /> <meta property="og:site_name" content="Center for Language and Speech Processing" /> <meta property="article:modified_time" content="2024-08-28T00:17:22+00:00" /> <meta property="og:image" content="https://www.clsp.jhu.edu/wp-content/uploads/2024/08/craig_nn1IdDn-1.jpg" /> <meta name="twitter:label1" content="Est. reading time" /> <meta name="twitter:data1" content="4 minutes" /> <meta name="twitter:label2" content="Written by" /> <meta name="twitter:data2" content="Ruth Scally" /> <script type="application/ld+json" class="yoast-schema-graph">{"@context":"https://schema.org","@graph":[{"@type":"WebPage","@id":"https://www.clsp.jhu.edu/people/affiliated-faculty/","url":"https://www.clsp.jhu.edu/people/affiliated-faculty/","name":"Affiliated Faculty - Center for Language and Speech Processing","isPartOf":{"@id":"https://www.clsp.jhu.edu/#website"},"primaryImageOfPage":{"@id":"https://www.clsp.jhu.edu/people/affiliated-faculty/#primaryimage"},"image":{"@id":"https://www.clsp.jhu.edu/people/affiliated-faculty/#primaryimage"},"thumbnailUrl":"https://www.clsp.jhu.edu/wp-content/uploads/2024/08/craig_nn1IdDn-1.jpg","datePublished":"2015-09-08T18:35:08+00:00","dateModified":"2024-08-28T00:17:22+00:00","breadcrumb":{"@id":"https://www.clsp.jhu.edu/people/affiliated-faculty/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https://www.clsp.jhu.edu/people/affiliated-faculty/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https://www.clsp.jhu.edu/people/affiliated-faculty/#primaryimage","url":"https://www.clsp.jhu.edu/wp-content/uploads/2024/08/craig_nn1IdDn-1.jpg","contentUrl":"https://www.clsp.jhu.edu/wp-content/uploads/2024/08/craig_nn1IdDn-1.jpg"},{"@type":"BreadcrumbList","@id":"https://www.clsp.jhu.edu/people/affiliated-faculty/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://www.clsp.jhu.edu/"},{"@type":"ListItem","position":2,"name":"People","item":"https://www.clsp.jhu.edu/people/"},{"@type":"ListItem","position":3,"name":"Affiliated Faculty"}]},{"@type":"WebSite","@id":"https://www.clsp.jhu.edu/#website","url":"https://www.clsp.jhu.edu/","name":"Center for Language and Speech Processing","description":"Johns Hopkins University","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://www.clsp.jhu.edu/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}</script>      <script type="text/javascript"> /* <![CDATA[ */ window._wpemojiSettings = {"baseUrl":"https:\/\/s.w.org\/images\/core\/emoji\/15.0.3\/72x72\/","ext":".png","svgUrl":"https:\/\/s.w.org\/images\/core\/emoji\/15.0.3\/svg\/","svgExt":".svg","source":{"concatemoji":"https:\/\/www.clsp.jhu.edu\/wp-includes\/js\/wp-emoji-release.min.js?ver=0db417805d51931f0f17316a3e6b0de3"}}; /*! 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<div class="span8"> <h1 class="page-title"> Affiliated Faculty </h1> <div class="the-content"> <p><strong><a href="https://www.clsp.jhu.edu/wp-content/uploads/2024/08/craig_nn1IdDn-1.jpg"><img decoding="async" class=" wp-image-27464 alignleft" src="https://www.clsp.jhu.edu/wp-content/uploads/2024/08/craig_nn1IdDn-1.jpg" alt="" width="147" height="145" /></a>Craig Messner</strong></p> <p style="text-align: left;"><strong><a href="/cdn-cgi/l/email-protection#4a29272f3939242f7e0a20223f642f2e3f"><span class="__cf_email__" data-cfemail="2546484056564b4011654f4d500b404150">[email protected]</span></a> | <a href="https://cdh.jhu.edu/people/5/">Website</a></strong></p> <p>I am a Lecturer in the Alexander Grass Humanities institute and Assistant Research Scientist in the Department of Computer Science. My research combines machine learning with traditional literary scholarship, and has focused on metric learning, the adaptation of large language models to historical domains, and analyzing orthographic variation in literary works. I am broadly interested in the set of topics that emerge at the nexus of literary scholarship, linguistics, and computational language modeling, particularly literary theory, semantics, pragmatics, and artificial neural networks. I can recommend as many obscure 19th century novels as you care to read.</p> <p><strong><a href="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Geraldine-Legendre-e1724750006493.jpg"><img decoding="async" class=" wp-image-8998 alignleft" src="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Geraldine-Legendre-e1724750006493.jpg" alt="Geraldine Legendre" width="142" height="160" /></a></strong><strong>Geraldine Legendre</strong></p> <p><strong><a href="/cdn-cgi/l/email-protection#cfa3aaa8aaa1abbdaa8faca0a8bcb8aca6e1a5a7bae1aaabba"><span class="__cf_email__" data-cfemail="97fbf2f0f2f9f3e5f2d7f4f8f0e4e0f4feb9fdffe2b9f2f3e2">[email protected]</span></a> | <a href="http://cogsci.jhu.edu/directory/geraldine-legendre/" target="_blank" rel="noopener">Website</a> | <a href="https://www.clsp.jhu.edu/publications/?tps=Geraldine+Legendre&tps_button=Search">Publications</a></strong></p> <p style="text-align: left;">I am a professor in the Cognitive Science department. My research touches on many aspects of theoretical linguistics, in particular: the role of optimization in syntax, architecture of the grammar, the syntax-semantic-phonology-morphology interfaces, and cross-linguistic variation. I am also interested in the early acquisition of language, in particular: functional categories, stages of development, the relation between early comprehension and production, and the general cognitive architecture underlying the language faculty. The first sound that I uttered as a baby was a fully formed sentence: “J’aime la façon dont vous souriez, parce que le soleil brille plus fort quand vous le faites.”</p> <p style="text-align: left;"><strong><a href="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Carey-Priebe.jpg"><img decoding="async" class="alignleft size-thumbnail wp-image-8997" src="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Carey-Priebe-150x150.jpg" alt="Carey Priebe" width="150" height="150" srcset="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Carey-Priebe-150x150.jpg 150w, https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Carey-Priebe.jpg 200w, https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Carey-Priebe-180x180.jpg 180w" sizes="(max-width: 150px) 100vw, 150px" /></a></strong></p> <p style="text-align: left;"><strong>Carey Priebe</strong></p> <p style="text-align: left;"><strong><a href="/cdn-cgi/l/email-protection#e2818792a2888a97cc878697"><span class="__cf_email__" data-cfemail="88ebedf8c8e2e0fda6edecfd">[email protected]</span></a> | <a href="http://www.ams.jhu.edu/~priebe/" target="_blank" rel="noopener">Website</a> | </strong><a href="https://www.clsp.jhu.edu/publications/?tps=Carey+Priebe&tps_button=Search"><strong>Publications</strong></a></p> <p style="text-align: left;">I am a professor in the Department of Applied Mathematics and Statistics. My research interests include computational statistics, kernel and mixture estimates, statistical pattern recognition, statistical image analysis, dimensionality reduction, model selection, and statistical inference for high-dimensional and graph data. There are tall tales saying that I own 100 acres of forest and that I do all my research on a chalkboard bolted to two trees in the very center of it. These stories are not true. I do some of my research elsewhere.</p> <p><strong><a href="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Paul-Smolensky-e1724750260690.jpg"><img loading="lazy" decoding="async" class=" wp-image-8996 alignleft" src="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Paul-Smolensky-e1724750260690-236x300.jpg" alt="Paul Smolensky" width="143" height="182" srcset="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Paul-Smolensky-e1724750260690-236x300.jpg 236w, https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Paul-Smolensky-e1724750260690.jpg 239w" sizes="auto, (max-width: 143px) 100vw, 143px" /></a>Paul Smolensky</strong></p> <p><strong><a href="/cdn-cgi/l/email-protection#33405e5c5f565d40584a73595b461d565746"><span class="__cf_email__" data-cfemail="f3809e9c9f969d80988ab3999b86dd969786">[email protected]</span></a> | <a href="http://cogsci.jhu.edu/directory/paul-smolensky/" target="_blank" rel="noopener">Website</a> | <a href="https://www.clsp.jhu.edu/publications/?tps=Paul+Smolensky&tps_button=Search">Publications</a></strong></p> <p>I am the Krieger-Eisenhower Professor of Cognitive Science. My primary research interests are in universal grammar and optimality Theory for phonology, syntax, acquisition, learnability, processing. My secondary research areas include the integration of connectionist (‘neural’) and symbolic computation, as well as computational, linguistic, and philosophical issues. I believe that the mind can effortlessly do matrix transformations and calculate eigenvectors. Mine can. Can yours?</p> <p><strong><a href="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Kyle-Rawlins.jpg"><img loading="lazy" decoding="async" class="wp-image-9014 alignleft" src="https://www.clsp.jhu.edu/wp-content/uploads/2015/09/Kyle-Rawlins.jpg" alt="Kyle Rawlins" width="165" height="129" /></a>Kyle Rawlins</strong></p> <p><strong><a href="/cdn-cgi/l/email-protection#6c1e0d1b0005021f2c0f030b1f0f054206041942090819"><span class="__cf_email__" data-cfemail="a8dac9dfc4c1c6dbe8cbc7cfdbcbc186c2c0dd86cdccdd">[email protected]</span></a> | <a href="https://rawlins.io" target="_blank" rel="noopener">Website</a></strong></p> <p>I am an Associate Professor and Director of Graduate Studies in the Cognitive Science Department department. My research is in natural language semantics and pragmatics, and is often interdisciplinary work involving the integration of theoretical linguistics, computational modeling, and crowd-sourced/experimental data.</p> <p><strong><a href="https://www.clsp.jhu.edu/wp-content/uploads/2024/08/JHU_faculty_photo_sirin.jpeg"><img loading="lazy" decoding="async" class="wp-image-27746 alignleft" src="https://www.clsp.jhu.edu/wp-content/uploads/2024/08/JHU_faculty_photo_sirin-288x300.jpeg" alt="" width="162" height="169" srcset="https://www.clsp.jhu.edu/wp-content/uploads/2024/08/JHU_faculty_photo_sirin-288x300.jpeg 288w, https://www.clsp.jhu.edu/wp-content/uploads/2024/08/JHU_faculty_photo_sirin-983x1024.jpeg 983w, https://www.clsp.jhu.edu/wp-content/uploads/2024/08/JHU_faculty_photo_sirin-768x800.jpeg 768w, https://www.clsp.jhu.edu/wp-content/uploads/2024/08/JHU_faculty_photo_sirin-1475x1536.jpeg 1475w, https://www.clsp.jhu.edu/wp-content/uploads/2024/08/JHU_faculty_photo_sirin-1966x2048.jpeg 1966w" sizes="auto, (max-width: 162px) 100vw, 162px" /></a></strong><strong>Hale Sirin</strong></p> <p><strong><a href="/cdn-cgi/l/email-protection#b8d0cbd1cad1d689f8d2d0cd96dddccd"><span class="__cf_email__" data-cfemail="9ef6edf7ecf7f0afdef4f6ebb0fbfaeb">[email protected]</span></a> | <a href="https://cdh.jhu.edu/people/4/">Website</a></strong></p> <p style="text-align: left;">I am an assistant professor at the Center for Digital Humanities. My research and teaching integrate computational and critical methods to explore questions about narrative and language in literary and historical documents. I focus on understanding the strengths and limitations of AI applications within cultural heritage domains.</p> <h3></h3> </div> </div> <div class="span4"> <div class="sidebar right-sidebar"> <div id="submenu-2" class="widget widget_submenu"><h3 class="widget-title"><a href="https://www.clsp.jhu.edu/faculty">People</a></h3> <div class="submenu"><ul class="sub-menu is-active-item"> <li class="menu-item menu-item-type-custom menu-item-object-custom current-menu-ancestor current-menu-parent menu-item-has-children menu-item-3998 is-active-item"><a href="https://www.clsp.jhu.edu/faculty">Faculty</a> <ul class="sub-menu is-active-item"> <li class="menu-item menu-item-type-post_type menu-item-object-page current-menu-item page_item page-item-4117 current_page_item menu-item-4134 is-active-item"><a href="https://www.clsp.jhu.edu/people/affiliated-faculty/" aria-current="page" class=" active-link">Affiliated Faculty</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-4131"><a href="https://www.clsp.jhu.edu/people/former-faculty/">Former Faculty</a></li> </ul> </li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-has-children menu-item-4132"><a href="https://www.clsp.jhu.edu/people/postdocs/">Postdocs</a> <ul class="sub-menu"> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-17465"><a href="https://www.clsp.jhu.edu/people/postdocs/">Current Postdocs</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-17460"><a href="https://www.clsp.jhu.edu/former-postdocs-and-research-staff/">Former Postdocs</a></li> </ul> </li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-4130"><a href="https://www.clsp.jhu.edu/people/graduate-students/">Graduate Students</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-4129"><a href="https://www.clsp.jhu.edu/people/staff/">Staff</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-4133"><a href="https://www.clsp.jhu.edu/people/alumni/">Alumni</a></li> </ul></div> </div><div id="text-7" class="widget widget_text"> <div class="textwidget"><a href="/cdn-cgi/l/email-protection#17646e7a6776577b7e646364397d7f39727362286462757d7274632a6462756474657e757237747b646764727a7e79766564">Subscribe to the CLSP Seminars Mailing List</a></div> </div><div id="ai1ec_agenda_widget-6" class="widget widget_ai1ec_agenda_widget"> <h3 class="widget-title">Upcoming Seminars</h3> <style>  </style> <div class="timely ai1ec-agenda-widget-view ai1ec-clearfix"> <div> <div class="ai1ec-date "> <a class="ai1ec-date-title ai1ec-load-view" href="https://www.clsp.jhu.edu/seminars/action~oneday/exact_date~12-2-2024/"> <div class="ai1ec-month">Dec</div> <div class="ai1ec-day">2</div> <div class="ai1ec-weekday">Mon</div> </a> <div class="ai1ec-date-events"> <div class="ai1ec-event ai1ec-event-id-28130 ai1ec-event-instance-id-3479 "> <a href="https://www.clsp.jhu.edu/events/kai-wei-chang-ucla-towards-trustworthy-ai-teaching-machines-through-language-and-rules/?instance_id=3479" class="ai1ec-popup-trigger ai1ec-load-event"> <span class="ai1ec-event-time"> 12:00 pm </span> <span class="ai1ec-event-title"> Kai-Wei Chang (UCLA) “Towards Tr... <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> </a> <div class="ai1ec-popover ai1ec-popup ai1ec-event-instance-id-3479"> <span class="ai1ec-popup-title"> <a href="https://www.clsp.jhu.edu/events/kai-wei-chang-ucla-towards-trustworthy-ai-teaching-machines-through-language-and-rules/?instance_id=3479" class="ai1ec-load-event" >Kai-Wei Chang (UCLA) “Towards Tr...</a> <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> <div class="ai1ec-event-time"> Dec 2 @ 12:00 pm – 1:15 pm </div> <div class="ai1ec-popup-excerpt">Abstract Over the past decades, machine learning has primarily relied on labeled data, with success often depending on the availability of vast, high-quality annotations and the assumption that test conditions mirror training conditions. In contrast,[...]</div> </div> </div> </div> </div> <div class="ai1ec-date "> <a class="ai1ec-date-title ai1ec-load-view" href="https://www.clsp.jhu.edu/seminars/action~oneday/exact_date~12-6-2024/"> <div class="ai1ec-month">Dec</div> <div class="ai1ec-day">6</div> <div class="ai1ec-weekday">Fri</div> </a> <div class="ai1ec-date-events"> <div class="ai1ec-event ai1ec-event-id-28133 ai1ec-event-instance-id-3482 "> <a href="https://www.clsp.jhu.edu/events/nanyun-peng-ucla-controllable-and-creative-natural-language-generation/?instance_id=3482" class="ai1ec-popup-trigger ai1ec-load-event"> <span class="ai1ec-event-time"> 12:00 pm </span> <span class="ai1ec-event-title"> Nanyun Peng (UCLA) “Controllable... <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> </a> <div class="ai1ec-popover ai1ec-popup ai1ec-event-instance-id-3482"> <span class="ai1ec-popup-title"> <a href="https://www.clsp.jhu.edu/events/nanyun-peng-ucla-controllable-and-creative-natural-language-generation/?instance_id=3482" class="ai1ec-load-event" >Nanyun Peng (UCLA) “Controllable...</a> <span class="ai1ec-event-location" >@ Hackerman Hall B17</span> </span> <div class="ai1ec-event-time"> Dec 6 @ 12:00 pm – 1:15 pm </div> <div class="ai1ec-popup-excerpt">Abstract Recent advances in large language models (LLMs) have achieved remarkable results across a wide range of natural language processing (NLP) applications, including text classification, summarization, machine translation, and dialogue systems. As LLMs grow increasingly[...]</div> </div> </div> </div> </div> </div> <div class="ai1ec-subscribe-buttons-widget"> <a class="ai1ec-btn ai1ec-btn-default ai1ec-btn-xs ai1ec-pull-right ai1ec-calendar-link" href="https://www.clsp.jhu.edu/seminars/"> View Calendar <i class="ai1ec-fa ai1ec-fa-arrow-right"></i> </a> <div class="ai1ec-subscribe-dropdown ai1ec-dropdown ai1ec-btn ai1ec-btn-default ai1ec-btn-xs"> <span role="button" class="ai1ec-dropdown-toggle ai1ec-subscribe" data-toggle="ai1ec-dropdown"> <i class="ai1ec-fa ai1ec-icon-rss ai1ec-fa-lg ai1ec-fa-fw"></i> <span class="ai1ec-hidden-xs"> Add <span class="ai1ec-caret"></span> </span> </span> <ul class="ai1ec-dropdown-menu ai1ec-pull-left" role="menu"> <li> <a class="ai1ec-tooltip-trigger ai1ec-tooltip-auto" target="_blank" data-placement="right" title="Copy this URL for your own Timely calendar or click to add to your rich-text calendar" 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data-nummobile="5" data-channel-subscribers="121 subscribers" data-subscribe-btn="1" data-subscribe-btn-text="Subscribe" data_channel_header_colors ="{"channelName":"","subscribeCount":"","buttonBackground":"","buttonText":""}" data-sby-flags="resizeDisable" data-postid="4117" data-sby-supports-lightbox="1" data-videocardlayout="vertical" > <div class="sby_items_wrap" style="padding: 5px;"> <div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_4N5EzdJATD4" data-date="1732290698" data-video-id="4N5EzdJATD4"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=4N5EzdJATD4" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/4N5EzdJATD4/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/4N5EzdJATD4\/sddefault.jpg"}" data-video-id="4N5EzdJATD4" data-video-title="Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo -- Roger Grosse"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Abstract Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence. In this work, we leverage the rich toolkit of Sequential Monte Carlo (SMC) for these probabilistic inference problems. In particular, we use learned twist functions to estimate the expected future value of the potential at each timestep, which enables us to focus inference-time computation on promising partial sequences. We propose a novel contrastive method for learning the twist functions, and establish connections with the rich literature of soft reinforcement learning. As a complementary application of our twisted SMC framework, we present methods for evaluating the accuracy of language model inference techniques using novel bidirectional SMC bounds on the log partition function. These bounds can be used to estimate the KL divergence between the inference and target distributions in both directions. We apply our inference evaluation techniques to show that twisted SMC is effective for sampling undesirable outputs from a pretrained model (a useful component of harmlessness training and automated red-teaming), generating reviews with varied sentiment, and performing infilling tasks. Biography Roger Grosse is an Associate Professor of Computer Science at the University of Toronto, Schwartz-Reisman Chair in Technology and Society, founding member of the Vector Institute for Artificial Intelligence, and a Member of Technical Staff on the Alignment Science team at Anthropic. His research focuses on using our understanding of deep learning to improve the safety and alignment of AI systems. He has held the Sloan Research Fellowship, Canada CIFAR AI Chair, and Canada Research Chair."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo -- Roger Grosse</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_cf4apIcnPtU" data-date="1732073606" data-video-id="cf4apIcnPtU"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=cf4apIcnPtU" target="_blank" rel="noopener" data-full-res="https://i4.ytimg.com/vi/cf4apIcnPtU/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/default.jpg","320":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/mqdefault.jpg","480":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/hqdefault.jpg","640":"https:\/\/i4.ytimg.com\/vi\/cf4apIcnPtU\/sddefault.jpg"}" data-video-id="cf4apIcnPtU" data-video-title="Generative World Explorer"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Project page: https://generative-world-explorer.github.io/ Paper: https://arxiv.org/abs/2411.11844 Abstract: Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their environment to update their beliefs about the world state. In contrast, humans can imagine unseen parts of the world through a mental exploration and revise their beliefs with imagined observations. Such updated beliefs can allow them to make more informed decisions, without necessitating the physical exploration of the world at all times. To achieve this human-like ability, we introduce the Generative World Explorer (Genex), an egocentric world exploration framework that allows an agent to mentally explore a large-scale 3D world (e.g., urban scenes) and acquire imagined observations to update its belief. This updated belief will then help the agent to make a more informed decision at the current step. To train Genex, we create a synthetic urban scene dataset, Genex-DB. Our experimental results demonstrate that (1) Genex can generate high-quality and consistent observations during long-horizon exploration of a large virtual physical world and (2) the beliefs updated with the generated observations can inform an existing decision-making model (e.g., an LLM agent) to make better plans."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Generative World Explorer</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_d5p6IhagfXM" data-date="1731992425" data-video-id="d5p6IhagfXM"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=d5p6IhagfXM" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/d5p6IhagfXM/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/d5p6IhagfXM\/sddefault.jpg"}" data-video-id="d5p6IhagfXM" data-video-title="Automatically Deriving Adjectival Scales – Marie-Catherine de Marneffe (OSU) - 2013"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="December 6, 2013 Abstract In this talk, I will discuss how to automatically derive the orderings and meanings of gradable adjectives. To determine whether the intended answer is “yes” or “no” in a dialogue such as “Was the movie wonderful? It was worth seeing”, we need to evaluate how “worth seeing” relates to “wonderful”. Can we automatically learn from real texts the scalar orderings people assign to these modifiers? I will show how we can exploit the availability of large amounts of text on the web (such as online reviews ratings) to approximate these orderings. Then I will turn to neural network language models. I will show that continuous space word representations extracted from such models can be used to derive adjectival scales of high quality, emphasizing that neural network language models do capture semantic regularities. I evaluate the quality of the adjectival scales on several datasets. Next, I will briefly turn to biomedical data: what does it mean to show “severe symptoms of cardiac disease” or “mild pulmonary symptoms”? I will outline work in progress targeting the meaning of gradable adjectives in that domain. Not only do we want to get an ordering between such adjectives, but we also want to learn what counts as “severe” or “mild” symptoms of a disease. Biography Marie-Catherine de Marneffe is an assistant professor in Linguistics at The Ohio State University. She received her PhD from Stanford University in December 2012 under the supervision of Christopher D. Manning. She is developing computational linguistic methods that capture what is conveyed by speakers beyond the literal meaning of the words they say. Primarily she wants to ground meanings in corpus data, and show how such meanings can drive pragmatic inference. She has also worked on Recognizing Textual Entailment and contributed to defining the Stanford Dependencies representation, which is designed to be a practical representation of grammatical relations and predicate argument structure"> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Automatically Deriving Adjectival Scales – Marie-Catherine de Marneffe (OSU) - 2013</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_uf6PSqy3WPc" data-date="1731645479" data-video-id="uf6PSqy3WPc"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=uf6PSqy3WPc" target="_blank" rel="noopener" data-full-res="https://i2.ytimg.com/vi/uf6PSqy3WPc/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/default.jpg","320":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/mqdefault.jpg","480":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/hqdefault.jpg","640":"https:\/\/i2.ytimg.com\/vi\/uf6PSqy3WPc\/sddefault.jpg"}" data-video-id="uf6PSqy3WPc" data-video-title="Evaluating Unlearning and Memorization via Compression -- Zhili Feng (CMU)"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Zhili Feng: https://zhilif.github.io/"> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >Evaluating Unlearning and Memorization via Compression -- Zhili Feng (CMU)</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 11.412-132.305s0-89.438-11.412-132.305zm-317.51 213.508V175.185l142.739 81.205-142.739 81.201z" class=""></path></svg> </div> <span class="sby_loader sby_hidden" style="background-color: rgb(255, 255, 255);"></span> </a> </div> </div> </div><div class="sby_item sby_new sby_transition" id="sby_UCWWiAEF7PMk-OK0-rwGITIg_X_U_jdXXfv0" data-date="1731004488" data-video-id="X_U_jdXXfv0"> <div class="sby_inner_item"> <div class="sby_video_thumbnail_wrap sby_item_video_thumbnail_wrap"> <a class="sby_video_thumbnail sby_item_video_thumbnail" href="https://www.youtube.com/watch?v=X_U_jdXXfv0" target="_blank" rel="noopener" data-full-res="https://i1.ytimg.com/vi/X_U_jdXXfv0/maxresdefault.jpg" data-img-src-set="{"120":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/default.jpg","320":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/mqdefault.jpg","480":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/hqdefault.jpg","640":"https:\/\/i1.ytimg.com\/vi\/X_U_jdXXfv0\/sddefault.jpg"}" data-video-id="X_U_jdXXfv0" data-video-title="AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (EMNLP 2024)"> <img decoding="async" src="https://www.clsp.jhu.edu/wp-content/plugins/feeds-for-youtube/img/placeholder.png" alt="Paper: https://arxiv.org/abs/2402.12370 Abstract: Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer this question, we propose AnaloBench, a benchmark to determine analogical reasoning ability in LMs. Our benchmarking approach focuses on aspects of this ability that are common among humans: (i) recalling related experiences from a large amount of information, and (ii) applying analogical reasoning to complex and lengthy scenarios. We test a broad collection of proprietary models (e.g., GPT family, Claude V2) and open source models such as LLaMA2. As in prior results, scaling up LMs results in some performance boosts. Surprisingly, scale offers minimal gains when, (i) analogies involve lengthy scenarios, or (ii) recalling relevant scenarios from a large pool of information, a process analogous to finding a needle in a haystack. We hope these observations encourage further research in this field."> <div class="sby_thumbnail_hover sby_item_video_thumbnail_hover"> <div class="sby_thumbnail_hover_inner"> <span class="sby_video_title" >AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (EMNLP 2024)</span> </div> </div> <div class="sby_play_btn" > <span class="sby_play_btn_bg"></span> <svg aria-hidden="true" focusable="false" data-prefix="fab" data-icon="youtube" role="img" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 576 512" class="svg-inline--fa fa-youtube fa-w-18"><path fill="currentColor" d="M549.655 124.083c-6.281-23.65-24.787-42.276-48.284-48.597C458.781 64 288 64 288 64S117.22 64 74.629 75.486c-23.497 6.322-42.003 24.947-48.284 48.597-11.412 42.867-11.412 132.305-11.412 132.305s0 89.438 11.412 132.305c6.281 23.65 24.787 41.5 48.284 47.821C117.22 448 288 448 288 448s170.78 0 213.371-11.486c23.497-6.321 42.003-24.171 48.284-47.821 11.412-42.867 11.412-132.305 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