Verified75% confidenceFactExact time
Responses from large language models are typically presented in complete sentences and an affirmative tone, lacking the linguistic signals such as "I'm not sure" or "this needs further verification" that human advisors customarily include.
3
Sources
75%
Confidence
Long-term
Relevance
10/1/2026
First Seen
Sources
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Related Claims
UnverifiedUsing a language model to judge objective factual verification—such as whether a tool call is correct or whether returned data is truthful—is mismatched with the role of language quality evaluation.77% similarVerifiedLarge language models have a miscalibration problem, often answering in a confident tone regardless of whether the answer is correct or incorrect.76% similarVerifiedPerplexity measures a language model's ability to predict test text; the lower the value, the less surprised the model is by the text.75% similarVerifiedThe training objective of current large language models is to generate responses that satisfy the user, not to generate correct responses.75% similarVerifiedLarge language models have a sycophancy characteristic, tending to organize their output according to the inclination of the prompt rather than reflecting factual truth.75% similar
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