Verified75% confidenceDecision RuleExact time
Calibration means that the probabilities output by the model should match reality; neural networks generally have an overconfidence tendency, so model probabilities should not be blindly trusted, and thresholds should be set only after validation with labeled data.
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10/13/2026
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VerifiedThe probability figures provided by guided generative models are essentially 'well-constrained inferences' rather than directly verifiable truths.78% similarUnverifiedLLM输出的置信度来自logits或token概率,是模型内部的相对偏好,并不等同于事件发生的真实频率,即模型可能过度自信。76% similarUnverifiedThe effectiveness of threshold functions is highly dependent on the calibration quality of the underlying model's output probabilities, where model calibration means that predicted probabilities should genuinely reflect the actual frequency of event occurrence.76% similarUnverifiedThe inference accuracy of this method depends on how closely the simulator approximates the true process; if the real reaction involves mechanisms not included in the simulation, the posterior estimates may be biased.75% similarUnverifiedPrecision is essentially computing, given that a prediction is positive, the posterior probability that it is truly positive.75% similar
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