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<div class="the-content"> <p>The Johns Hopkins Center for Language and Speech Processing (CLSP) is an interdisciplinary research and educational center focused on the science and technology of language and speech. Within its field, CLSP is recognized as one of the largest and most influential academic research centers in the world. The center conducts research across a broad spectrum of fundamental and applied topics including acoustic processing, automatic speech recognition, big data, cognitive modeling, computational linguistics, information extraction, machine learning, machine translation, and text analysis. It is home to over sixty researchers, including faculty, research scientists, postdocs, graduate students, and undergraduates drawn from the departments of cognitive science, computer science, electrical and computer engineering, and mathematical sciences. Linguistics is an active area of research in the Cognitive Science Department; see <a href="https://cogsci.jhu.edu/research-areas/linguistics/" target="_blank" rel="noopener noreferrer">the overview of their linguistics focus</a> for more details.</p> <p>As part of its educational mission, CLSP coordinates a full complement of <a href="https://www.clsp.jhu.edu/courses/">courses</a> dealing with a diverse array of topics in language and speech. It also offers a weekly <a href="https://www.clsp.jhu.edu/seminars/">seminar</a> featuring prominent speakers in speech and language processing. Finally, it is the host of the widely-known CLSP summer research <a href="https://www.clsp.jhu.edu/workshops/">workshop</a>, an event drawing researchers at all levels (faculty to undergraduate) from around the world to conduct intensive research on fundamental problems. Hosted annually since 1995, the workshop has produced many important advances in speech and language technology.</p> <p>CLSP was established in 1992 with support from the US Government (NSF, DARPA, DoD) and grew to prominence under the directorship of the late Frederick Jelinek (<a href="http://eng.jhu.edu/wse/Fred_Jelinek/memory/pages" target="_blank" rel="noopener noreferrer">1932-2010</a>). In 2007 CLSP gained a sibling, the Johns Hopkins Human Language Technology Center of Excellence (HLTCOE), a government-funded research center investigating all aspects of advanced language technology. HLTCOE and its researchers are tightly integrated into CLSP.</p> </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/about/">About</a></h3> <div class="submenu"><ul class="sub-menu"> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-18654"><a href="https://www.clsp.jhu.edu/about/diversity-and-inclusion/">Diversity and Inclusion</a></li> <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-19580 has-icon "><a href="https://covidinfo.jhu.edu/health-safety/"><span class="fontawesome-text">Health + Safety: Coronavirus Information</span> <i class="icon-external"> </i></a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-4146"><a href="https://www.clsp.jhu.edu/contact/">Contact Us/Visit Us</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-9666"><a href="https://www.clsp.jhu.edu/workshops/internships/">Internships</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-5835"><a href="https://www.clsp.jhu.edu/courses/">Courses</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-4144"><a href="https://www.clsp.jhu.edu/apply-for-phd/">Apply for PhD</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-11014"><a href="https://www.clsp.jhu.edu/about/jelinek-fellowship/">Jelinek Fellowship</a></li> <li class="menu-item menu-item-type-post_type menu-item-object-page menu-item-4145"><a href="https://www.clsp.jhu.edu/employment-opportunities/">Employment Opportunities</a></li> <li class="menu-item menu-item-type-custom menu-item-object-custom menu-item-23742 has-icon "><a href="https://secure.jhu.edu/form/eng"><span class="fontawesome-text">Make a Donation</span> <i class="icon-external"> </i></a></li> </ul></div> </div><div id="text-7" class="widget widget_text"> <div class="textwidget"><a href="/cdn-cgi/l/email-protection#86f5ffebf6e7c6eaeff5f2f5a8eceea8e3e2f3b9f5f3e4ece3e5f2bbf5f3e4f5e5f4efe4e3a6e5eaf5f6f5e3ebefe8e7f4f5">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="4098" 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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