Verified80% confidenceFactExact time
Perplexity measures a language model's ability to predict test text; the lower the value, the less surprised the model is by the text.
4
Sources
80%
Confidence
Long-term
Relevance
8/19/2026
First Seen
Sources
Related Claims
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.80% similarUnverifiedLarge language models autoregressively predict the next highest-probability token, causing AI-generated text to have low perplexity.78% similarUnverified自回归语言模型的OOD检测比传统机器学习更困难，因为语言组合空间庞大、语义OOD与表面形式OOD不对应，且对抗者可构造统计上看似in-distribution但语义触发OOD的输入77% similarUnverifiedText generated by modern LLMs is increasingly difficult to distinguish from human writing in terms of grammar and style, posing challenges to the accuracy of AI detection tools based on textual features.74% similar
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