Verified65% confidenceCautionExact time
When a model lacks up-to-date information, it may not admit ignorance, but instead fabricate seemingly plausible yet factually incorrect answers (hallucinations).
3
Sources
65%
Confidence
Long-term
Relevance
9/2/2026
First Seen
Sources
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Related Claims
UnverifiedPrompts cannot supply a model with knowledge it does not already possess; they can only make it more honestly silent when it is ignorant.79% similarUnverifiedWhen a model lacks accurate knowledge of a particular library, it tends to generate syntactically correct but semantically incorrect code—so-called 'confident errors.'79% similarVerifiedWhen the model itself lacks the capability to understand a specific problem, no amount of prompt engineering can compensate for this deficiency78% similarUnverifiedOne of the deep root causes of model hallucination is that the model cannot distinguish whether it is retrieving facts or fabricating content, due to a lack of genuine access to its own cognitive state.74% similarUnverifiedThe study observed a clear lack of correlation between the correctness of answers and the validity of reasoning traces, with models frequently arriving at correct answers even when the reasoning traces are completely invalid.74% similar
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