Unverified60% confidenceFactTime unknown
Large language models commonly suffer from overconfidence and require prompt engineering and calibration strategies to calibrate confidence levels.
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60%
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Long-term
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6/1/2026
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Related Claims
UnverifiedHaving the model assess its own confidence is unreliable, so multiple checks are needed.76% similarUnverifiedNeural network models tend to be overconfident, and their probability outputs do not faithfully reflect the credibility of predictions, exhibiting calibration problems.76% similarVerifiedCurrent mainstream large language models exhibit significant deficiencies in both confidence calibration and selective abstention, tend to be overconfident, and when faced with questions outside the training distribution, tend to produce fluent but incorrect answers.76% similarUnverifiedFor high-reliability scenarios such as evidence-based medicine, a small model that honestly expresses uncertainty may be more practical than an overconfident large model—this represents a tradeoff.75% similarUnverifiedHigh confidence does not equal high truthfulness; a model may state fabricated numbers with extremely high confidence.74% similar
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