Unverified50% confidenceFactExact time
Neural network models tend to be overconfident, and their probability outputs do not faithfully reflect the credibility of predictions, exhibiting calibration problems.
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50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
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Related Claims
UnverifiedLarge language models commonly suffer from overconfidence and require prompt engineering and calibration strategies to calibrate confidence levels.76% similarUnverifiedTreating LLMs as genuine intelligence leads to overestimating their reliability, mistakenly trusting their reasoning capabilities, and lacking vigilance regarding the hallucination problem.73% 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.72% similarUnverifiedThe model's tool-triggering decision is essentially a probabilistic threshold problem: when confidence exceeds the threshold the model tends to answer directly, and only triggers retrieval when confidence falls below it.72% similar
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