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-21487@www.clsp.jhu.edu DTSTAMP:20240328T151941Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nEnormous amounts of ever-changing knowledge are avai lable online in diverse textual styles and diverse formats. Recent advance s in deep learning algorithms and large-scale datasets are spurring progre ss in many Natural Language Processing (NLP) tasks\, including question an swering. Nevertheless\, these models cannot scale up when task-annotated t raining data are scarce. This talk presents my lab’s work toward building general-purpose models in NLP and how to systematically evaluate them. Fir st\, I present a general model for two known tasks of question answering i n English and multiple languages that are robust to small domain shifts. Then\, I show a meta-training approach that can solve a variety of NLP tas ks with only using a few examples and introduce a benchmark to evaluate cr oss-task generalization. Finally\, I discuss neuro-symbolic approaches to address more complex tasks by eliciting knowledge from structured data and language models.\n\nBiography\n\nHanna Hajishirzi is an Assistant Profess or in the Paul G. Allen School of Computer Science & Engineering at the Un iversity of Washington and a Senior Research Manager at the Allen Institut e for AI. Her research spans different areas in NLP and AI\, focusing on d eveloping general-purpose machine learning algorithms that can solve many NLP tasks. Applications for these algorithms include question answering\, representation learning\, green AI\, knowledge extraction\, and conversati onal dialogue. Honors include the NSF CAREER Award\, Sloan Fellowship\, Al len Distinguished Investigator Award\, Intel rising star award\, best pape r and honorable mention awards\, and several industry research faculty awa rds. Hanna received her PhD from University of Illinois and spent a year a s a postdoc at Disney Research and CMU. DTSTART;TZID=America/New_York:20220225T120000 DTEND;TZID=America/New_York:20220225T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 SEQUENCE:0 SUMMARY:Hanna Hajishirzi (University of Washington & Allen Institute for AI ) “Toward Robust\, Knowledge-Rich NLP” URL:https://www.clsp.jhu.edu/events/hanna-hajishirzi-university-of-washingt on-allen-institute-for-ai-toward-robust-knowledge-rich-nlp/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nNatural language provides an intuitive and powerful i nterface to access knowledge at scale. Modern language systems draw inform ation from two rich knowledge sources: (1) information stored in their par ameters during massive pretraining and (2) documents retrieved at inferenc e time. Yet\, we are far from building systems that can reliably provide i nformation from such knowledge sources. In this talk\, I will discuss path s 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 co mpressor that maps retrieved documents into textual summaries prior to in- context integration. This not only reduces the computational costs but als o filters irrelevant or incorrect information. In the second half of the t alk\, I will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated i nformation by identifying facts that are prone to rapid change. I will con clude my talk by proposing an interactive system that can elicit informati on from users when needed.
\nBiography
\nEunsol Choi is an assistant professor in the Computer Scie nce department at the University of Texas at Austin. Prior to UT\, she spe nt a year at Google AI as a visiting researcher. Her research area spans n atural language processing and machine learning. She is particularly inter ested in interpreting and reasoning about text in a dynamic real world con text. She is a recipient of a Facebook research fellowship\, Google facult y research award\, Sony faculty award\, and an outstanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from Unive rsity of Washington and B.A in mathematics and computer science from Corne ll University.
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