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-22394@www.clsp.jhu.edu DTSTAMP:20240328T215333Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nModel robustness and spurious correlations have received increasing atten tion in the NLP community\, both in methods and evaluation. The term “spur ious correlation” is overloaded though and can refer to any undesirable sh ortcuts learned by the model\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we oft en (implicitly) assume that a spurious feature is irrelevant for predictio n. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying obj ects in an image. In contrast\, they carry important information that’s ne eded to make predictions by humans. In this talk\, we argue that it is mor e productive to characterize features in terms of their necessity and suff iciency for prediction. We then discuss the implications of this categoriz ation in representation\, learning\, and evaluation.
\nBiogr aphy
\nHe He is an Assistant Professor in the Department of Computer Science and the Center for Data Science at New York University. She obtained her PhD in Computer Science at the University of Maryland\, C ollege Park. Before joining NYU\, she spent a year at AWS AI and was a pos t-doc at Stanford University before that. She is interested in building ro bust and trustworthy NLP systems in human-centered settings. Her recent re search focus includes robust language understanding\, collaborative text g eneration\, and understanding capabilities and issues of large language mo dels.
\n 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-TAGS;LANGUAGE=en-US:2022\,He\,October END:VEVENT BEGIN:VEVENT UID:ai1ec-24481@www.clsp.jhu.edu DTSTAMP:20240328T215333Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nNatural language provides an intuitive and powerful interface to access knowledge at scale. Modern l anguage systems draw information from two rich knowledge sources: (1) info rmation stored in their parameters during massive pretraining and (2) docu ments retrieved at inference time. Yet\, we are far from building systems that can reliably provide information from such knowledge sources. In this talk\, I will discuss paths for more robust systems. In the first part of the talk\, I will present a module for scaling retrieval-based knowledge augmentation. We learn a compressor that maps retrieved documents into tex tual summaries prior to in-context integration. This not only reduces the computational costs but also filters irrelevant or incorrect information. In the second half of the talk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent mode ls from reciting outdated information by identifying facts that are prone to rapid change. I will conclude my talk by proposing an interactive syste m that can elicit information from users when needed.
\nBiog raphy
\nEunsol Choi is an assistant pro fessor in the Computer Science department at the University of Texas at Au stin. Prior to UT\, she spent a year at Google AI as a visiting researcher . Her research area spans natural language processing and machine learning . She is particularly interested in interpreting and reasoning about text in a dynamic real world context. She is a recipient of a Facebook research fellowship\, Google faculty research award\, Sony faculty award\, and an outstanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from University of Washington and B.A in mathematics and computer science from Cornell University.
\nDTSTART;TZID=America/New_York:20240315T120000 DTEND;TZID=America/New_York:20240315T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21209 SEQUENCE:0 SUMMARY:Eunsol Choi (University of Texas at Austin) “Knowledge-Rich Languag e Systems in a Dynamic World” URL:https://www.clsp.jhu.edu/events/eunsol-choi-university-of-texas-at-aust in-knowledge-rich-language-systems-in-a-dynamic-world/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,Choi\,March END:VEVENT END:VCALENDAR