Unverified50% confidenceFactExact time
logprob is log probability: when the token probability is 0.9, logprob is about -0.105, and when the probability is 0.01, it is about -4.605; the closer the value is to 0, the more confident the model is.
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50%
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Long-term
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10/22/2026
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Related Claims
UnverifiedShannon pointed out that if an event has n equally probable possible outcomes, the minimum amount of information needed to determine the specific outcome is log₂(n) bits.74% 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.73% similarUnverifiedA logit difference of 1 corresponds to a probability ratio of about e≈2.718, so a +2 logit bias multiplies that token's weight relative to other tokens by about 7.4x.72% similarUnverifiedApplying a logit bias of -2 reduces the probability of the target tokens to approximately 1/7 of the original (e^-2 ≈ 0.135).71% similarUnverifiedThe log-score (i.e., cross-entropy) penalizes extreme errors more aggressively, and tends toward negative infinity when a model predicts the wrong answer with a probability close to 1.0.69% similar
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