Reasoning over Sets: Toward Consistent LLM Systems
Event Description
Abstract: Modern large language models (LLMs) increasingly operate not on single inputs, but over sets of information--collections of generated statements, retrieved documents, or intermediate reasoning steps. However, existing frameworks largely treat these elements independently, overlooking the fact that correctness and reliability often emerge at the set level, where interactions such as contradictions, redundancy, or joint relevance become critical. In this talk, I argue that reasoning over sets is a fundamental challenge for building reliable and controllable LLM systems, and present a unified perspective centered on set-level verification, retrieval, and editing.
Bio: Jay-Yoon Lee is an associate professor in the Graduate School of Data Science at
Seoul National University. The goal of his research is injecting knowledge/constraints into neural models, primarily for natural language processing (NLP) tasks. He is broadly interested in structured prediction, multi-task learning, logical reasoning, and better representation learning for the aforementioned topics. Currently, he is exploring these topics at the Graduate School of Data Science (GSDS), Seoul National University (SNU), with SKI-ML lab members. Prior to joining SNU he was a postdoc associate at IESL with Professor Andrew McCallum and during his Ph.D. with his advisor Jaime Carbonell.
Location: NCS 120
