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:20240328T180350Z 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
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\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-22423@www.clsp.jhu.edu DTSTAMP:20240328T180350Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20221007T120000 DTEND;TZID=America/New_York:20221007T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Ariya Rastrow (Amazon) URL:https://www.clsp.jhu.edu/events/ariya-rastrow-amazon-2/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,October\,Rastrow END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240328T180350Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\n\nModel robustness and spurious correlations have rec eived increasing attention in the NLP community\, both in methods and eval uation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by the model\, as judged by domain e xperts.\n\n\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, man y features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image . In contrast\, they carry important information that’s needed to make pre dictions by humans. In this talk\, we argue that it is more productive to characterize features in terms of their necessity and sufficiency for pred iction. We then discuss the implications of this categorization in represe ntation\, learning\, and evaluation.\nBiography\nHe He is an Assistant Pro fessor in the Department of Computer Science and the Center for Data Scien ce at New York University. She obtained her PhD in Computer Science at the University of Maryland\, College Park. Before joining NYU\, she spent a y ear at AWS AI and was a post-doc at Stanford University before that. She i s interested in building robust and trustworthy NLP systems in human-cente red settings. Her recent research focus includes robust language understan ding\, collaborative text generation\, and understanding capabilities and issues of large language models. DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” URL:https://www.clsp.jhu.edu/events/he-he-new-york-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModel robustness and spuri ous correlations have received increasing attention in the NLP community\, both in methods and evaluation. The term “spurious correlation” is overlo aded though and can refer to any undesirable shortcuts learned by the mode l\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contra st\, they carry important information that’s needed to make predictions by humans. In this talk\, we argue that it is more productive to characteriz e features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation\, l earning\, and evaluation.
\nBiography
\nHe He is an Assistant Professor in the Department of Computer Science and the C enter for Data Science at New York University. She obtained her PhD in Com puter Science at the University of Maryland\, College Park. Before joining NYU\, she spent a year at AWS AI and was a post-doc at Stanford Universit y before that. She is interested in building robust and trustworthy NLP sy stems in human-centered settings. Her recent research focus includes robus t language understanding\, collaborative text generation\, and understandi ng capabilities and issues of large language models.
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\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.
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\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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