Unverified50% confidenceFactExact time
Large language models do not inherently possess calibrated probability estimation capabilities, and the probability values output by a model do not represent actual occurrence rates.
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50%
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Long-term
Relevance
10/7/2026
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Related Claims
UnverifiedLarge model outputs are probabilistic and non-deterministic; running the same prompt on the same version of a model multiple times may produce different results, making quality judgments in gradual rollouts difficult.73% similarUnverifiedDeep learning models are generally prone to overconfidence, where the probability values output by a model do not truly reflect the actual probability of its predictions being correct — a phenomenon known as poor calibration.70% similarUnverifiedThe original material does not publicly disclose detailed experimental result data or specific code details, making it impossible to provide a quantitative evaluation of the model's actual performance.69% similarUnverifiedThe final rankings of the models could not be predicted by parameter size, price, vendor, or token consumption.68% similarUnverifiedThe correlation between token-level probabilities (logprob) output by a model and factual accuracy is not strong, and fabricated facts may receive the same high generation probability as true information.67% similar
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