CS&E Colloquium: Illuminating Generative AI: Mapping Knowledge in Large Language Models

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m. This week's speaker, Abhilasha Ravichander (University of Washington), will be giving a talk titled "Illuminating Generative AI: Mapping Knowledge in Large Language Models."

Abstract

Millions of everyday users are interacting with technologies built with generative AI, such as voice assistants, search engines, and chatbots. While these AI-based systems are being increasingly integrated into modern life, they can also magnify risks, inequities, and dissatisfaction when providers deploy unreliable systems. A primary obstacle to having reliable systems is the opacity of the underlying large language models— we lack a systematic understanding of how models work, where critical vulnerabilities may arise, why they are happening, and how models must be redesigned to address them. In this talk, I will first describe my work in investigating large language models to illuminate when models acquire knowledge and capabilities. Then, I will describe my work on building methods to enable data transparency for large language models, that allows practitioners to make sense of the information available to models. Finally, I will describe my work on understanding why large language models produce incorrect knowledge, and implications for building the next generation of responsible AI systems. 

Biography

Abhilasha Ravichander is a postdoctoral scholar at the University of Washington, advised by Professor Yejin Choi.  She received her PhD from Carnegie Mellon University in 2022. Her research spans natural language processing, machine learning, and artificial intelligence, with a focus on improving the robustness and interpretability of large-scale language models.  Abhilasha’s work has been presented at several top NLP conferences, receiving Best Resource Paper Award at ACL 2024, Best Theme Paper Award at ACL 2024, Best Paper Award at the Mid-Atlantic Student Colloquium 2024, Best Paper Award at the SoCalNLP 2022 symposium, and Area Chair Favorite Paper award at COLING 2018.  She has been recognized as a "Rising Star in Generative AI" (2024), "Rising Star in EECS" (2022), and "Rising Star in Data Science" (2021).