Unverified50% confidenceFactExact time
Model self-assessment almost always gives a high score, and that score actually measures confidence rather than quality; control must come from outside the loop that produces the results.
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10/19/2026
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UnverifiedIn 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.79% similarUnverifiedThe expected value of a proper scoring rule is maximized only when the probability stated by the model is consistent with the true likelihood.79% similarUnverifiedBroad quality-rubric criteria cause the reviewer model to agree with the generator model too often, because both models share the same optimistic priors.78% similarUnverifiedMany models still exhibit high confidence in incorrect answers, presenting a confidence calibration problem.77% similarVerifiedA model with slightly lower accuracy but well-calibrated confidence is often more suitable for production deployment than a model with higher accuracy that is blindly overconfident.77% similar
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