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We introduce a method to measure uncertainty in large language models. Fortasks like question answering, it is essential to know when we can trust thenatural language outputs of foundation models. We show that measuringuncertainty in natural language is challenging because of "semanticequivalence" -- different sentences can mean the same thing. To overcome thesechallenges we introduce semantic entropy -- an entropy which incorporateslinguistic invariances created by shared meanings. Our method is unsupervised,uses only a single model, and requires no modifications to off-the-shelflanguage models. In comprehensive ablation studies we show that the semanticentropy is more predictive of model accuracy on question answering data setsthan comparable baselines.

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation