ENS-Data Science colloquium – Noah A. Smith (University of Washington)

“Language models are the only thing we have in natural language processing that could be considered scientific.” A collaborator of mine said this more than a decade ago, long before LMs emerged as the single most important technology to come out of our field. In these exciting times, I seek both to make the study of LMs more scientific, and to make LMs more practically beneficial. In this talk, I’ll first draw from recent work from my UW group that starts to tackle questions about LMs that could help “break them down” for a deeper scientific understanding. Then I’ll turn to some developments that try to broaden the usefulness of language models by literally “breaking them down” into more modular components. Finally, I’ll shamelessly advertise some newly delivered artifacts that I believe will help the research community make progress on both of these directions and more.

Speaker’s bio: Noah Smith is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science & Engineering at the University of Washington (also Adjunct in Linguistics, Affiliate of the Center for Statistics and the Social Sciences, and Senior Data Science Fellow at the eScience Institute), as well as Senior Director of NLP Research at the Allen Institute for Artificial Intelligence. His work is at the junction of natural language processing (NLP), machine learning (ML), and computational social science, and spans core problems in NLP, general-purpose ML methods for NLP, methodology in NLP, and a wide range of applications. He recently wrote Language Models: A Guide for the Perplexed, a general-audience tutorial.