Disincentivizing Hallucination @SimonsInstitute
Disincentivizing Hallucination  @SimonsInstitute
Uploaded May 2026 | Updated September 2026, 1 week ago
Adam Kalai (OpenAI)
https://simons.berkeley.edu/talks/adam-kalai-openai-2026-05-26
The Role of TCS in Modern Machine Learning

We explain how hallucinations in language models are rise in the first place. Prior work has shown how to modify models to reduce hallucinations. We also argue that the reason hallucinations persist is that benchmarks inadvertently reward guessing when unsure, and we discuss incentive compatible ways to modify the benchmarks.

Joint work with Santosh Vempala, Ofir Nachum and Edwin Zhang.
Disincentivizing HallucinationWindowed thinning and query complexity for the bouncy particle and Zigzag samplersFedOpt for LLMsClassical algorithms for quantum Gibbs statesAn introduction to the hardness versus randomness paradigmThe Many Faces of Heterogeneity: Federated, Continual, and Modular LearningRandom hyperbolic surfacesRandomness Extractors and Related ObjectsTalk by Micah Sheller (Flower/ML Commons)Talk by Eva Dyer (University of Pennsylvania)Other BeingsTheory of Modern AI: Learning Theoretic, Game Theoretic, and Algorithmic PerspectivesFederated Learning in the Generative AI Era
Simons Institute for the Theory of Computing |

Disincentivizing Hallucination

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