SILO: Do Large Language Models Need Statistical Foundations?

In this talk, we advocate for the development of rigorous statistical foundations for large language models (LLMs). We begin by elaborating two key features that motivate statistical perspectives for LLMs: (1) the probabilistic, autoregressive nature of next-token prediction, and (2) the complexity and black box nature of Transformer architectures. To illustrate how statistical insights can directly benefit LLM development and applications, we present two concrete examples. First, we demonstrate statistical inconsistencies and biases arising from the current approach to aligning LLMs with human preference. We propose a regularization term for aligning LLMs that is both necessary and sufficient to ensure consistent alignment. Second, we introduce a novel statistical framework to analyze the efficiency of watermarking schemes, with a focus on a watermarking scheme developed by OpenAI for which we derive optimal detection rules that outperform existing ones. Collectively, these findings showcase how statistical insights can address pressing challenges in LLMs while simultaneously illuminating new research avenues for the broader statistical community to advance responsible generative AI research. This talk is based on arXiv:2405.16455, 2404.01245, and 2503.10990.

Bio: 

Weijie Su is an Associate Professor in the Wharton Statistics and Data Science Department and, by courtesy, in the Departments of Computer and Information Science and Mathematics at the University of Pennsylvania. He is a co-director of Penn Research in Machine Learning (PRiML) Center. Prior to joining Penn, he received his Ph.D. in Statistics from Stanford University in 2016 and bachelor’s degree in Mathematics from Peking University in 2011. His research interests span the statistical foundations of generative AI, privacy-preserving machine learning, high-dimensional statistics, and optimization. He serves as an associate editor of the Journal of Machine Learning Research, Journal of the American Statistical Association, Foundations and Trends in Statistics, and Operations Research, and he is currently guest editing a special issue on Statistics for Large Language Models and Large Language Models for Statistics in Stat. His work has been recognized with several awards, such as the Stanford Anderson Dissertation Award, NSF CAREER Award, Sloan Research Fellowship, IMS Peter Hall Prize, SIAM Early Career Prize in Data Science, ASA Noether Early Career Award, and the ICBS Frontiers of Science Award in Mathematics.