Talk: Modeling Language as Social and Cultural Data

Live stream: https://uwmadison.zoom.us/j/94703971780?pwd=5TxFeHFcihL6Y9fXJWA7fOc3aTzSEy.1

Abstract:  Language models (LMs) are powerful because they embed social identities and beliefs. Their increasing capabilities have expanded the disciplinary overlap between AI and other fields, including those in the social sciences and humanities. My talk will illustrate how I've built reciprocal relationships between natural language processing (NLP) and two other fields: sociolinguistics and education. I'll discuss how a sociolinguistic lens can inform model development, by surfacing implicit social preferences of pretraining data curation practices. In return, LMs can answer sociolinguistic research questions, uncovering the social dynamics of language at billion-word scale. Within education, I will discuss how LMs can support content analyses of school curricula. Then, I'll show how I leverage educators’ in-domain expertise to create challenging multimodal benchmarks. Altogether, my work emphasizing social aspects of language contributes to both human-centered model development and empirical studies of social and cultural media.

Bio:   Lucy Li is a Ph.D. candidate at the University of California, Berkeley, affiliated with Berkeley AI Research and the School of Information. Her research intersects natural language processing with computational social science and digital humanities (e.g. cultural analytics). She has worked with Microsoft Research’s Fairness, Accountability, Transparency, and Ethics (FATE) team and the Allen Institute for AI, and led collaborations with colleagues in education, psychology, and English literature. She has been recognized by EECS Rising Stars, Rising Stars in Data Science, an American Educational Research Association (AERA) Best Paper Award, and an NSF Graduate Research Fellowship.