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-21280@www.clsp.jhu.edu DTSTAMP:20240328T112124Z 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-21621@www.clsp.jhu.edu DTSTAMP:20240328T112124Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.\nBiography\nAlane Su hr is a PhD Candidate in the Department of Computer Science at Cornell Uni versity\, advised by Yoav Artzi. Her research spans natural language proc essing\, machine learning\, and computer vision\, with a focus on building systems that participate and continually learn in situated natural langua ge interactions with human users. Alane’s work has been recognized by pape r awards at ACL and NAACL\, and has been supported by fellowships and gran ts\, including an NSF Graduate Research Fellowship\, a Facebook PhD Fellow ship\, and research awards from AI2\, ParlAI\, and AWS. Alane has also co- organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane received a BS in Computer Science and Engineering as an Eminence Fellow at the Ohio State University. DTSTART;TZID=America/New_York:20220314T120000 DTEND;TZID=America/New_York:20220314T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Alane Suhr (Cornell University) “Reasoning and Learning in Interact ive Natural Language Systems” URL:https://www.clsp.jhu.edu/events/alane-suhr-cornell-university-reasoning -and-learning-in-interactive-natural-language-systems/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nSystems that support expressive\, situated natural la nguage interactions are essential for expanding access to complex computin g systems\, such as robots and databases\, to non-experts. Reasoning and l earning in such natural language interactions is a challenging open proble m. For example\, resolving sentence meaning requires reasoning not only ab out word meaning\, but also about the interaction context\, including the history of the interaction and the situated environment. In addition\, the sequential dynamics that arise between user and system in and across inte ractions make learning from static data\, i.e.\, supervised data\, both ch allenging and ineffective. However\, these same interaction dynamics resul t in ample opportunities for learning from implicit and explicit feedback that arises naturally in the interaction. This lays the foundation for sys tems that continually learn\, improve\, and adapt their language use throu gh interaction\, without additional annotation effort. In this talk\, I wi ll focus on these challenges and opportunities. First\, I will describe ou r work on modeling dependencies between language meaning and interaction c ontext when mapping natural language in interaction to executable code. In the second part of the talk\, I will describe our work on language unders tanding and generation in collaborative interactions\, focusing on continu al learning from explicit and implicit user feedback.
\nBiog raphy
\nAlane Suhr is a PhD Candidate in the Department of Computer Science at Cornell University\, advised by Yoav Artzi. Her resea rch spans natural language processing\, machine learning\, and computer vi sion\, with a focus on building systems that participate and continually l earn in situated natural language interactions with human users. Alane’s w ork has been recognized by paper awards at ACL and NAACL\, and has been su pported by fellowships and grants\, including an NSF Graduate Research Fel lowship\, a Facebook PhD Fellowship\, and research awards from AI2\, ParlA I\, and AWS. Alane has also co-organized multiple workshops and tutorials appearing at NeurIPS\, EMNLP\, NAACL\, and ACL. Previously\, Alane receive d a BS in Computer Science and Engineering as an Eminence Fellow at the Oh io State University.
\n X-TAGS;LANGUAGE=en-US:2022\,March\,Suhr END:VEVENT BEGIN:VEVENT UID:ai1ec-23886@www.clsp.jhu.edu DTSTAMP:20240328T112124Z 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.
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