Unverified50% confidenceConceptExact time
The confidence number a model reports about itself and a calibrated probability are not the same kind of object: the self-reported number comes from token sampling in the same forward pass as the answer, with no lookup of outcomes, whereas a calibrated probability must come from a paired record (join) of predictions and outcomes.
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50%
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Long-term
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10/15/2026
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UnverifiedThe expected value of a proper scoring rule is maximized only when the probability stated by the model is consistent with the true likelihood.77% similarUnverifiedIn prediction markets, confidence usually denotes the subjective probability a model assigns to a binary or multinomial outcome and can be calibrated after the fact using metrics such as the Brier score; in LLM-driven systems it is often obtained from model self-assessment or by averaging the logits of multiple samples, with reliability far lower than that of statistical models.76% similarUnverifiedModel calibration refers to the degree to which a model's output probabilities match its actual accuracy rate; for a well-calibrated model, if the probability it gives for a certain type of question is 0.8, then about 80% of such questions are indeed answered correctly.75% similarUnverifiedConfidence is typically represented by the product of per-token probabilities or the sum of their logarithms.75% similarUnverifiedConformal prediction provides finite-sample validity guarantees without making parametric assumptions about the data distribution, meaning that prediction intervals cover the true value at a frequency of at least 1-α at confidence level α.74% similar
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