Auditing, Understanding, and Evaluating Large Language Models

Title: Auditing, Understanding, and Evaluating Large Language Models

Abstract: The widespread adoption of large language models places a responsibility on the AI research community to rigorously study and understand them. In this talk, I will describe my group’s research on analyzing language models’ training data, internal mechanisms, and downstream behavior. First, I will discuss two complementary approaches to audit usage of copyrighted data for language model training. Next, I will describe my group’s recent work on understanding how language models work internally, including a case study of how they use Fourier features to solve arithmetic problems. Finally, I will highlight our collaborative efforts to characterize LLMs’ strengths and weaknesses in application domains spanning medicine, robotics, and software engineering.

Bio: Robin Jia is an Assistant Professor of Computer Science at the University of Southern California. He received his Ph.D. in Computer Science from Stanford University, where he was advised by Percy Liang. He has also spent time as a visiting researcher at Facebook AI Research, working with Luke Zettlemoyer and Douwe Kiela. He is interested broadly in natural language processing and machine learning, with a focus on scientifically understanding NLP models. Robin’s work has received best paper awards at ACL and EMNLP.