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My talk will cover my work bridging natural language processing (NLP) with two research areas: sociolinguistics and education.
Abstract: New advancements in large language models (LMs) have expanded the disciplinary overlap between AI and other fields, including those in the social sciences and humanities. For models and systems to be useful for a wide range of users and tasks, AI research’s relationships with these fields should be reciprocal, in which we inform each other. My talk will cover my work bridging natural language processing (NLP) with two research areas: sociolinguistics and education. To show how a sociolinguistic lens can support NLP, I’ll discuss my work surfacing implicit social preferences of LMs’ pretraining data curation practices. I’ll also show ways in which NLP methods can answer sociolinguistic questions around English variation in digital communities at scale. To illustrate how NLP can support education research, I’ll describe how I use LMs to conduct content analyses of books taught in schools. Then, I will discuss how I incorporate educators’ in-domain expertise to create new datasets for evaluating AI models. To conclude, I’ll present my future vision for human-centered model development and text-as-data studies of 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.
Events are free and open to the public unless otherwise noted.