Verified65% confidenceFactExact time
Large language models can approximately estimate decision confidence by analyzing token probability distributions, explicitly outputting self-evaluation scores, or observing answer consistency across multiple samplings.
3
Sources
65%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
AI Agent人在回路（HITL）设计：风险分级与最佳实践
Related Claims
UnverifiedTraditional large language model evaluation centers on the accuracy of single-turn question answering, scoring the output answer against a reference answer after a question is given80% similarVerifiedThe essence of large language models is learning the conditional probability distribution of what the next word is given the preceding text80% similarVerifiedWhen generating each token, large language models perform probabilistic sampling over all possible next words in the vocabulary, rather than deterministically selecting a unique answer.78% similarUnverified口语化采样通过在提示词中要求模型生成一组候选答案并显式说出每个答案对应的概率分布来提升多样性77% similarUnverifiedDue to the stochasticity of large model outputs, evaluation practices commonly employ methods such as model scoring (LLM-as-Judge), human annotation, or reference-based semantic similarity.77% similar
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