Just asking questions (MIT)

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• Jacob Andreas (MIT)
• Thursday 20 November 2025, 14:00-15:00
• https://cam-ac-uk.zoom.us/j/97599459216?pwd=QTRsOWZCOXRTREVnbTJBdXVpOXFvdz09.

If you have a question about this talk, please contact Lucas Resck.

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 EECS and CSAIL . He completed his PhD at Berkeley, where he was a member of the Berkeley NLP Group and the Berkeley AI Research Lab. He has also spent time with the Cambridge NLIP Group, the NLP Group, and the former Center for Computational Learning Systems at Columbia. His research focuses on language as a communicative and computational tool. He studies how people learn to understand and generate novel utterances with remarkably little data and how language facilitates the acquisition of new concepts and reasoning. Recognizing that current machine learning techniques fall short of human abilities in language understanding and learning from language, his work aims to uncover the computational foundations of language learning and develop general-purpose intelligent systems capable of effective human communication and learning from human guidance.

This talk is part of the Language Technology Lab Seminars series.

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