Unverified50% confidenceFactExact time
The probability calibration of model outputs is often biased, especially on patterns that appear familiar within the training data distribution, where overly high confidence is given.
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10/18/2026
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UnverifiedA theoretical study proves that a model can perfectly match energies and forces on the training set yet produce completely wrong statistical behavior when sampling, that is, the distribution-level error does not decrease as local prediction accuracy improves.79% similarUnverifiedMost general-purpose large models suffer from overconfidence issues, where their output probabilities reflect token distribution preferences more than genuine uncertainty estimates.78% similarUnverifiedFitting the propensity score in-sample induces dependence among individual contributions, so variance estimators derived under the i.i.d. assumption underestimate the true uncertainty.76% 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.76% 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% similar
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