Semantic entropy improves uncertainty estimation in natural language generation by incorporating semantic equivalences, outperforming standard entropy baselines on predicting model accuracy for question answering.
Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness
2 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.
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Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
Semantic entropy improves uncertainty estimation in natural language generation by incorporating semantic equivalences, outperforming standard entropy baselines on predicting model accuracy for question answering.
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Language Models (Mostly) Know What They Know
Language models show good calibration when asked to estimate the probability that their own answers are correct, with performance improving as models get larger.