Kathleen McKeown, Columbia University

Abstract

Large language modeling has changed the nature of natural language processing, with striking success in a large variety of tasks. Despite this success, failures exist, raising questions about their readiness for real use. In this talk I explore two problems for large language models: hallucination and bias. I discuss hallucination in the context of summarization and I present bias in the context of understanding African American Language.

[Video]

Speaker bio: Kathleen R. McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University and the Founding Director of the Data Science Institute, serving as Director from 2012 to 2017. In earlier years, she served as Department Chair (1998-2003) and as Vice Dean for Research for the School of Engineering and Applied Science (2010-2012). A leading scholar and researcher in the field of natural language processing, McKeown focuses her research on the use of data for societal problems; her interests include text summarization, question answering, natural language generation, social media analysis and multilingual applications. She has received numerous honors and awards, including 2023 IEEE Innovation in Societal Infrastructure Award, American Philosophical Society Elected member, American Academy of Arts and Science elected member, American Association of Artificial Intelligence Fellow, a Founding Fellow of the Association for Computational Linguistics and an Association for Computing Machinery Fellow. Early on she received the National Science Foundation Presidential Young Investigator Award, and a National Science Foundation Faculty Award for Women. In 2010, she won both the Columbia Great Teacher Award—an honor bestowed by the students—and the Anita Borg Woman of Vision Award for Innovation.