Title: Good Old-fashioned LLMs (or, Autoformalizing the World)
Abstract:
Classical formal approaches to artificial intelligence, based on manipulation of symbolic structures, have a number of appealing properties---they generalize (and fail) in predictable ways, provide interpretable traces of behavior, and can be formally verified or manually audited for correctness. Why are they so rarely used in the modern era? One of the major challenges in the development of symbolic AI systems is what McCarthy called the "frame problem": the impossibility of enumerating a set of symbolic rules that fully characterize the behavior of every system in every circumstance. Modern deep learning approaches avoid this representational challenge, but at the cost of interpretability, robustness, and sample-efficiency. How do we build learning systems that are as flexible as neural models but as understandable and generalizable as symbolic ones? In this talk, I'll describe a recent line of work aimed at automatically building "just-in-time" formal models tailored to be just expressive enough to solve tasks of interest In this approach, neural sequence models pre-trained on text and code are used to place priors over symbolic model descriptions, which are then verified and refined interactively---yielding symbolic graphics libraries that can be used to solve image understanding problems, or symbolic planning representations for sequential decision-making. Here natural language turns out to play a central role as an intermediate representation linking neural and symbolic computation, and I'll conclude with some very recent work on using symbolic reasoning to improve the coherence and factual accuracy of language models themselves.
Bio:
Jacob Andreas is an associate professor at MIT in the Department of Electrical Engineering and Computer Science as well as the Computer Science and Artificial Intelligence Laboratory. His research aims to build intelligent systems that can communicate effectively using language and learn from human guidance. Jacob earned his Ph.D. from UC Berkeley, his M.Phil. from Cambridge (where he studied as a Churchill scholar) and his B.S. from Columbia. He has been named a Kavli Fellow by the National Academy of Sciences, and has received the NSF CAREER award, MIT's Junior Bose and Kolokotrones teaching awards, and paper awards at ACL, ICML and NAACL.
