Unverified50% confidenceFactExact time
The language model's confidence is a self-assessed score (0 to 1), not a calibrated probability; the model may give high confidence to wrong answers and may give low scores to correct answers, so it should be treated as evidence rather than permission.
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10/13/2026
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UnverifiedThe model showed the highest confidence (95%) on underdetermined problems (problems with no unique correct answer), exhibiting a reverse calibration phenomenon.75% similarUnverifiedThe study found that models' accuracy on semantically plausible distorted statements is actually higher than their performance on meaningless random replacements.74% 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.73% similarUnverifiedThe academic community refers to the phenomenon of significant deviation between a model's confidence and its actual accuracy as Poor Calibration.72% similarUnverifiedMany models still exhibit high confidence in incorrect answers, presenting a confidence calibration problem.71% similar
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