Verified65% confidenceFactExact time
When a model lacks accurate knowledge of a particular library, it tends to generate syntactically correct but semantically incorrect code—so-called 'confident errors.'
3
Sources
65%
Confidence
Long-term
Relevance
7/17/2026
First Seen
Sources
Related Claims
VerifiedWhen a model lacks up-to-date information, it may not admit ignorance, but instead fabricate seemingly plausible yet factually incorrect answers (hallucinations).79% similarUnverifiedThe confidence level of a language model and the reliability of its judgments are often not correlated, posing the risk of hallucinatory overconfidence.78% similarUnverifiedModels lacking self-verification tend to fall into a cycle of confident errors, making it difficult to reliably complete multi-step end-to-end tasks even when initial generation accuracy is relatively high.77% similarUnverifiedCurrent 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% similarUnverifiedLarge language models exhibit an unfaithful execution problem: Opus sometimes arbitrarily changes definitions on its own, and upon finding that results do not match, writes code to override the derived results and directly fills in the answer it considers correct.76% similar
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