The seminar will begin at 12:00 p.m. Refreshments will be available starting at 1:15 p.m.
Abstract
Natural language provides an intuitive and powerful interface to access knowledge at scale. Modern language systems draw information from two rich knowledge sources: (1) information stored in their parameters during massive pretraining and (2) documents retrieved at inference time. Yet we are far from building systems that can reliably provide information from such knowledge sources. In this talk, Eunsol Choi will discuss paths for more robust systems. In the first part of her talk, she will present a module for scaling retrieval-based knowledge augmentation, learning a compressor that maps retrieved documents into textual 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 her talk, she will discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated information by identifying facts that are prone to rapid change. She will conclude her talk by proposing an interactive system that can elicit information from users when needed.
Speaker Biography
Eunsol Choi is an assistant professor of computer science at the University of Texas (UT) at Austin. Prior to teaching at UT, she spent a year at Google AI as a visiting researcher. Choi’s 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 Meta Research PhD Fellowship, a Google Faculty Research Award, a Sony Research Award, and an Outstanding Paper Award at the Conference on Empirical Methods in Natural Language Processing. She received a PhD in computer science and engineering from the University of Washington and a BA in mathematics and computer science from Cornell University.