Abstract: In the age of deep networks, “learning” almost invariably means “learning from examples”. We train language models with human-generated text and labeled preference pairs, image classifiers with large datasets of images, and robot policies with rollouts or demonstrations. When human learners acquire new concepts and skills, we often do so with richer supervision, especially in the form of language—we learn new concepts from examples accompanied by descriptions or definitions, and new skills from demonstrations accompanied by instructions. Crucially, language-based supervision involves not only instructions but *questions*—students ask questions to elicit the most useful pieces of supervision, and teachers ask questions to probe student knowledge and encourage them to acquire new skills or aspects of understanding on their own. This talk will focus on a few recent projects focused on building computational models that can ask good questions for both learning and teaching, with applications spanning LM alignment, policy learning, and education. This is joint work with Belinda Li, Alex Tamkin, Noah Goodman, Andi Peng, Ilia Sucholutsky, Nishanth Kumar, Julie A Shah, Andreea Bobu, Alexis Ross, Gabe Grand, Valerio Pepe and Josh Tenenbaum.

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 understand the computational foundations of language learning, and to build intelligent systems that can 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 received a Sloan fellowship, an NSF CAREER award, MIT’s Junior Bose and Kolokotrones teaching awards, and paper awards at ACL, ICML and NAACL.