Webinar: Belief, Uncertainty, and Truth in Language Models

What does it mean for a language model to “know” something — and how should it communicate uncertainty to the people who use it? In this talk, Jacob Andreas, associate professor of electrical engineering and computer science at MIT, will explore new approaches to building language models that not only model the world but also model themselves.

Andreas will show how optimizing for coherence and calibration—beyond accuracy alone—can produce models that are both more factually consistent and more reliable in expressing confidence. These advances raise pressing questions for governance: How should large models present information? What standards should guide their expression of reliability or doubt?

Moderated by Josh Joseph, the Berkman Klein Center’s chief AI scientist, this conversation will situate cutting-edge technical work on belief and uncertainty in language models within the wider debates about interpretability, trust, and the responsible use of AI. Navigating computer science and the questions it raises for broader society, the conversation will conclude by examining the policy implications raised by these complex issues.