Unverified50% confidenceFactExact time
In 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.
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10/12/2026
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UnverifiedConfidence is typically represented by the product of per-token probabilities or the sum of their logarithms.76% similarUnverifiedMost general-purpose large models suffer from overconfidence issues, where their output probabilities reflect token distribution preferences more than genuine uncertainty estimates.76% similarUnverifiedConfidence values typically originate from the model's softmax probability distribution over each option — a large probability gap yields a high confidence value, while a uniform distribution yields a low one.75% similarUnverifiedThe expected value of a proper scoring rule is maximized only when the probability stated by the model is consistent with the true likelihood.74% similarUnverifiedIn AI systems, confidence is typically expressed as a probability value between 0 and 1, reflecting the model's estimate of the correctness of its own output.74% similar
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