Verified65% confidenceFactExact time
困惑度（Perplexity）是自然语言处理领域衡量语言模型对文本意外程度的核心指标，困惑度越低文本越可预测
3
Sources
65%
Confidence
Long-term
Relevance
7/9/2026
First Seen
Sources
How Zhuque AI Detection Works: A Guide for Content Creators on Reducing Traffic Restrictions
bilibili宋哥讲AI图文7/8/2026
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UnverifiedText generated by large language models tends to have low perplexity, with word choices that are overly consistent with probabilistic expectations, lacking the unexpectedness of human writing.78% similarUnverifiedPerplexity能够提供带引用来源的答案，部分解决了大语言模型的幻觉问题75% similarUnverifiedPerplexity measures a language model's ability to predict test text; the lower the value, the less surprised the model is by the text.69% similarUnverifiedLarge language models have a miscalibration problem, often answering in a confident tone regardless of whether the answer is correct or incorrect.67% similarPartially Verified当前自然语言提示的最大问题在于歧义性，同样的需求不同人写出的提示词可能完全不同，AI产出的代码也可能截然不同66% similar
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