BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21277@www.clsp.jhu.edu DTSTAMP:20240328T160639Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAs humans\, our understanding of language is grounded in a rich mental model about “how the world works” – that we learn throug h perception and interaction. We use this understanding to reason beyond w hat we literally observe or read\, imagining how situations might unfold i n the world. Machines today struggle at this kind of reasoning\, which lim its how they can communicate with humans.In my talk\, I will discuss three lines of work to bridge this gap between machines and humans. I will firs t discuss how we might measure grounded understanding. I will introduce a suite of approaches for constructing benchmarks\, using machines in the lo op to filter out spurious biases. Next\, I will introduce PIGLeT: a model that learns physical commonsense understanding by interacting with the wor ld through simulation\, using this knowledge to ground language. From an E nglish-language description of an event\, PIGLeT can anticipate how the wo rld state might change – outperforming text-only models that are orders of magnitude larger. Finally\, I will introduce MERLOT\, which learns about situations in the world by watching millions of YouTube videos with transc ribed speech. Through training objectives inspired by the developmental ps ychology idea of multimodal reentry\, MERLOT learns to fuse language\, vis ion\, and sound together into powerful representations.Together\, these di rections suggest a path forward for building machines that learn language rooted in the world.\nBiography\nRowan Zellers is a final year PhD candida te at the University of Washington in Computer Science & Engineering\, adv ised by Yejin Choi and Ali Farhadi. His research focuses on enabling machi nes to understand language\, vision\, sound\, and the world beyond these m odalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 outstanding paper award. His work has appeared in several me dia outlets\, including Wired\, the Washington Post\, and the New York Tim es. In the past\, he graduated from Harvey Mudd College with a B.S. in Com puter Science & Mathematics\, and has interned at the Allen Institute for AI. DTSTART;TZID=America/New_York:20220214T120000 DTEND;TZID=America/New_York:20220214T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Rowan Zellers (University of Washington) ” Grounding Language by Se eing\, Hearing\, and Interacting” URL:https://www.clsp.jhu.edu/events/rowan-zellers-university-of-washington- grounding-language-by-seeing-hearing-and-interacting/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nAs humans\, our understanding of language is grounded
in a rich mental model about “how the world works” – that we learn throug
h perception and interaction. We use this understanding to reason beyond w
hat we literally observe or read\, imagining how situations might unfold i
n the world. Machines today struggle at this kind of reasoning\, which lim
its how they can communicate with humans.
In my talk\, I will discuss three lines of work to bridge
this gap between machines and humans. I will first discuss how we might m
easure grounded understanding. I will introduce a suite of approaches for
constructing benchmarks\, using machines in the loop to filter out spuriou
s biases. Next\, I will introduce PIGLeT: a model that learns physical com
monsense understanding by interacting with the world through simulation\,
using this knowledge to ground language. From an English-language descript
ion of an event\, PIGLeT can anticipate how the world state might change –
outperforming text-only models that are orders of magnitude larger. Final
ly\, I will introduce MERLOT\, which learns about situations in the world
by watching millions of YouTube videos with transcribed speech. Through tr
aining objectives inspired by the developmental psychology idea of multimo
dal reentry\, MERLOT learns to fuse language\, vision\, and sound together
into powerful representations.
Together\, these directions suggest a path forward for building mac
hines that learn language rooted in the world.
Biography strong>
\nRowan Zellers is a final year PhD candidate at the Univers ity of Washington in Computer Science & Engineering\, advised by Yejin Cho i and Ali Farhadi. His research focuses on enabling machines to understand language\, vision\, sound\, and the world beyond these modalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 out standing paper award. His work has appeared in several media outlets\, inc luding Wired\, the Washington Post\, and the New York Times. In the past\, he graduated from Harvey Mudd College with a B.S. in Computer Science & M athematics\, and has interned at the Allen Institute for AI.
\n< /HTML> X-TAGS;LANGUAGE=en-US:2022\,February\,Zellers END:VEVENT BEGIN:VEVENT UID:ai1ec-21280@www.clsp.jhu.edu DTSTAMP:20240328T160639Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAs AI-driven language interfaces (such as chat-bots) become more integrated into our lives\, they need to become more versatile and reliable in their communication with human users. How can we make pro gress toward building more “general” models that are capable of understand ing a broader spectrum of language commands\, given practical constraints such as the limited availability of labeled data?\nIn this talk\, I will d escribe my research toward addressing this question along two dimensions o f generality. First I will discuss progress in “breadth” — models that add ress a wider variety of tasks and abilities\, drawing inspiration from exi sting statistical learning techniques such as multi-task learning. In part icular\, I will showcase a system that works well on several QA benchmarks \, resulting in state-of-the-art results on 10 benchmarks. Furthermore\, I will show its extension to tasks beyond QA (such as text generation or cl assification) that can be “defined” via natural language. In the second p art\, I will focus on progress in “depth” — models that can handle complex inputs such as compositional questions. I will introduce Text Modular Net works\, a general framework that casts problem-solving as natural language communication among simpler “modules.” Applying this framework to composi tional questions by leveraging discrete optimization and existing non-comp ositional closed-box QA models results in a model with strong empirical pe rformance on multiple complex QA benchmarks while providing human-readable reasoning.\nI will conclude with future research directions toward broade r NLP systems by addressing the limitations of the presented ideas and oth er missing elements needed to move toward more general-purpose interactive language understanding systems.\nBiography\nDaniel Khashabi is a postdoct oral researcher at the Allen Institute for Artificial Intelligence (AI2)\, Seattle. Previously\, he completed his Ph.D. in Computer and Information Sciences at the University of Pennsylvania in 2019. His interests lie at t he intersection of artificial intelligence and natural language processing \, with a vision toward more general systems through unified algorithms an d theories. DTSTART;TZID=America/New_York:20220218T120000 DTEND;TZID=America/New_York:20220218T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Khashabi (Allen Institute for Artificial Intelligence) “The Quest Toward Generality in Natural Language Understanding” URL:https://www.clsp.jhu.edu/events/daniel-khashabi-allen-institute-for-art ificial-intelligence/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAs AI-driven language interfaces (such as c hat-bots) become more integrated into our lives\, they need to become more versatile and reliable in their communication with human users. How can w e make progress toward building more “general” models that are capable of understanding a broader spectrum of language commands\, given practical co nstraints such as the limited availability of labeled data?
\nIn this talk\, I will describe my research toward addressing this ques tion along two dimensions of generality. First I will discuss progress in “breadth” — models that address a wider variety of tasks and abilities\, d rawing inspiration from existing statistical learning techniques such as m ulti-task learning. In particular\, I will showcase a system that works we ll on several QA benchmarks\, resulting in state-of-the-art results on 10 benchmarks. Furthermore\, I will show its extension to tasks beyond QA (su ch as text generation or classification) that can be “defined” via natural language. In the second part\, I will focus on progress in “depth” — mod els that can handle complex inputs such as compositional questions. I will introduce Text Modular Networks\, a general framework that casts problem- solving as natural language communication among simpler “modules.” Applyin g this framework to compositional questions by leveraging discrete optimiz ation and existing non-compositional closed-box QA models results in a mod el with strong empirical performance on multiple complex QA benchmarks whi le providing human-readable reasoning.
\nI will conclude w ith future research directions toward broader NLP systems by addressing th e limitations of the presented ideas and other missing elements needed to move toward more general-purpose interactive language understanding system s.
\nBiography
\nDaniel Khashabi is a postdoctoral researcher at the Allen Institute for Artificia l Intelligence (AI2)\, Seattle. Previously\, he completed his Ph.D. in Com puter and Information Sciences at the University of Pennsylvania in 2019. His interests lie at the intersection of artificial intelligence and natur al language processing\, with a vision toward more general systems through unified algorithms and theories.
\n X-TAGS;LANGUAGE=en-US:2022\,February\,Khashabi END:VEVENT BEGIN:VEVENT UID:ai1ec-23886@www.clsp.jhu.edu DTSTAMP:20240328T160639Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nThe arms race to build increasingly larger\, powerful language models (LMs) in the past year has been remarkable. Yet incorpora ting LMs effectively into practical applications that facilitate manual wo rkflows remains challenging. I will discuss LMs’ limiting factors and our efforts to overcome them. I will start with challenges surrounding efficie nt and robust LM alignment. I will share insights from our recent paper “S elf-Instruct” (ACL 2023)\, where we used vanilla (unaligned) LMs for align ing itself\, an approach that has yielded some success. Then\, I will move on to the challenge of tracing the output of LMs to reliable sources\, a weakness that makes them prone to hallucinations. I will discuss our recen t approach of ‘according-to’ prompting\, which steers LMs to quote directl y from sources observed in its pre-training. If time permits\, I will disc uss our ongoing project to adapt LMs to interact with web pages. Throughou t the presentation\, I will highlight our progress\, and end with question s about our future progress.\nBiography\nDaniel Khashabi is an assistant p rofessor in computer science at Johns Hopkins University and the Center fo r Language and Speech Processing (CLSP) member. He is interested in buildi ng reasoning-driven modular NLP systems that are robust\, transparent\, an d communicative\, particularly those that use natural language as the comm unication medium. Khashabi has published over 40 papers on natural languag e processing and AI in top-tier venues. His work touches upon developing. His research has won the ACL 2023 Outstanding Paper Award\, NAACL 2022 Bes t Paper Award\, research gifts from the Allen Institute for AI\, and an Am azon Research Award 2023. Before joining Hopkins\, he was a postdoctoral f ellow at the Allen Institute for AI (2019-2022) and obtained a Ph.D. from the University of Pennsylvania in 2019. DTSTART;TZID=America/New_York:20230908T120000 DTEND;TZID=America/New_York:20230908T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Khashabi (Johns Hopkins University) “Building More Helpful L anguage Models” URL:https://www.clsp.jhu.edu/events/daniel-khashabi-johns-hopkins-universit y/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nThe arms race to build increasingly larger\, powerful language models (LMs) in the past year has been remarkable. Yet incorpora ting LMs effectively into practical applications that facilitate manual wo rkflows remains challenging. I will discuss LMs’ limiting factors and our efforts to overcome them. I will start with challenges surrounding efficie nt and robust LM alignment. I will share insights from our recent paper “Self-Instruct” (ACL 2023)\, where we used vanilla (unaligned) LMs for aligning itself\, an approach that has yielded some success. Then\, I will move on to the challenge of t racing the output of LMs to reliable sources\, a weakness that makes them prone to hallucinations. I will discuss our recent approach of ‘according-to’ prompting\, which steers LM s to quote directly from sources observed in its pre-training. If time per mits\, I will discuss our ongoing project to adapt LMs to interact with we b pages. Throughout the presentation\, I will highlight our progress\, and end with questions about our future progress.
\nBiography strong>
\nDaniel Khashabi is an assistant professor in computer science at Johns Hopkins University and the Center for Language and Speech Pr ocessing (CLSP) member. He is interested in building reasoning-driven modu lar NLP systems that are robust\, transparent\, and communicative\, partic ularly those that use natural language as the communication medium. Khasha bi has published over 40 papers on natural language processing and AI in t op-tier venues. His work touches upon developing. His research has won the ACL 2023 Outstanding Paper Award\, NAACL 2022 Best Paper Award\, research gifts from the Allen Institute for AI\, and an Amazon Research Award 2023 . Before joining Hopkins\, he was a postdoctoral fellow at the Allen Insti tute for AI (2019-2022) and obtained a Ph.D. from the University of Pennsy lvania in 2019.
\n X-TAGS;LANGUAGE=en-US:2023\,Khashabi\,September END:VEVENT END:VCALENDAR