Can LLMs Reason and Plan?
Large Language Models (LLMs) are on track to reverse what seemed like an inexorable shift of AI from explicit to tacit knowledge tasks. Trained as they are on everything ever written on the web, LLMs exhibit “approximate omniscience”–they can provide answers to all sorts of queries, but with nary a guarantee. This could herald a new era for knowledge-based AI systems–with LLMs taking the role of (blowhard?) experts. Listen in to Subbarao Kambhampati, professor of computer science at Arizona State University, who will reify this vision and attendant caveats in the context of the role of LLMs in planning tasks.
Experience the Episode
Presented by The Lucy Family Institute for Data & Society
Friday, May 9, 2025 12:00 pm
Large Language Models (LLMs) are on track to reverse what seemed like an inexorable shift of AI from explicit to tacit knowledge tasks. Trained as they are on everything ever written on the web, LLMs exhibit “approximate omniscience”–they can provide answers to all sorts of queries, but with nary a guarantee. This could herald a new era for knowledge-based AI systems–with LLMs taking the role of (blowhard?) experts.
But first, we have to stop confusing the impressive form of the generated knowledge for correct content, and resist the temptation to ascribe reasoning, planning, self-critiquing etc. powers to approximate retrieval by these n-gram models on steroids. We have to focus instead on LLM-Modulo techniques that complement the unfettered idea generation of LLMs with careful vetting by model-based AI systems.
Join us for the first session of the second cohort of the Soc(AI)ety Seminars as we host Subbarao Kambhampati, professor of computer science at Arizona State University. In this talk, Kambhampati will reify this vision and attendant caveats in the context of the role of LLMs in planning tasks.
For more information visit the event website.
Meet the Speaker: Subbarao Kambhampati
Subbarao Kambhampati is a professor of computer science in the School of Computing and Augmented Intelligence at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems. He is a fellow of Association for the Advancement of Artificial Intelligence, American Association for the Advancement of Science, and Association for Computing machinery, and was an NSF Young Investigator. He was the president of the Association for the Advancement of Artificial Intelligence, trustee of International Joint Conference on Artificial Intelligence, and a founding board member of Partnership on AI. Kambhampati’s research as well as his views on the progress and societal impacts of AI have been featured in multiple national and international media outlets. He writes a column on the societal and policy implications of the advances in Artificial Intelligence for The Hill.