Symbolic Reasoning for Large Language Models
- Lamarr
- RC Trust
Prof. Dr. Guy Van den Broeck from the Computer Science Department at University of California LA, USA
Abstract - Many expect that AI will solve society’s problems by simply being more intelligent than we are. Implicit in this bullish perspective is the assumption that AI will naturally learn to reason from data: that it can form trains of thought that “make sense”, similar to how a human expert might reason about a case, or more formally, how a mathematician might prove a theorem. This talk will investigate the question whether this behavior can be learned from data, and how we can design the next generation of AI techniques that can achieve such capabilities. It will focus on neurosymbolic reasoning for large language models, both at training and generation time, using probabilistic circuits as the architecture that bridges learning and reasoning.
Prof. Dr. Guy Van den Broeck
Bio - Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the StarAI lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His papers have been recognized with awards from key conferences such as AAAI, UAI, KR, and OOPSLA. Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.