Symbolic Reasoning in the Age of Large Language Models
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Abstract:
Today, reasoning is commonly interpreted as large language models generating chains of thought. Yet historically, AI reasoning had a very different meaning: executing algorithms that manipulated symbols to perform logical or probabilistic deduction and derive definite answers to questions about knowledge. In this talk, I show that such old-fashioned ideas are very relevant to reasoning with large language models today. In particular, I will demonstrate that integrating symbolic reasoning algorithms directly into the architecture of language models enables state-of-the-art capabilities in controllable text generation, alignment, and mathematical reasoning. These capabilities are built on top of tractable probabilistic circuit models that approximate the distribution of the large language model's future behavior, and allow for efficient reasoning on the GPU.
Bio:
Guy Van den Broeck is a Professor in UCLA's Computer Science Department, where he directs the StarAI Lab. His research lies at the intersection of machine learning, knowledge representation, and reasoning. His contributions have earned awards from leading conferences, including 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.