Refreshments are available starting at 10:30 a.m. The seminar will begin at 10:45 a.m.
Abstract
Language models are primarily trained via imitation on massive amounts of human data; as a result, they’re capable of performing a wide range of tasks, but often lack the deep reasoning capabilities of classic AI systems like Deep Blue and AlphaGo. In this talk, Nicholas Tomlin will first present core technical challenges related to “reasoning with language,” using his work on computer crossword solvers as a running example. Then, he will show how methods for “interactive reasoning” can enable human-AI teams to solve complex problems jointly. Finally, he will discuss his work on “explainable reasoning,” where the goal is to explain the decisions made by expert AI systems like AlphaGo in human-interpretable terms. Tomlin will conclude by sharing his views on the future of language model reasoning, agents, and interactive systems.
Speaker Biography
Nicholas Tomlin is a final-year PhD student in the Berkeley NLP Group at the University of California, Berkeley, where he is advised by Dan Klein. Tomlin’s work focuses primarily on reasoning and multi-agent interaction with language models. He has co-created systems such as the Berkeley Crossword Solver, the first superhuman computer crossword solver, as well as Ghostbuster, a state-of-the-art method for large language model detection. His work has been supported by grants from the NSF and FAR.AI and has received media coverage from outlets such as Discover, WIRED, and the BBC.