Unverified60% confidenceFactExact time
Large language models autoregressively predict the next highest-probability token, causing AI-generated text to have low perplexity.
2
Sources
60%
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
Related Claims
UnverifiedWhen large language models generate text, the optimization target is the probability of the next token, not the reader's comprehension efficiency.78% similarVerifiedPerplexity measures a language model's ability to predict test text; the lower the value, the less surprised the model is by the text.78% similarUnverifiedExplicitly constraining the AI in the prompt to 'use only the transcript below' can significantly reduce the probability of large language models hallucinating (fabricating content that does not exist in the transcript).77% similarUnverifiedCurrent mainstream large language models are essentially performing text prediction, calculating the most likely next word based on existing context, with no understanding, intent, or consciousness.77% similarVerifiedWhen generating text, language models default to selecting word sequences with higher conditional probability.76% similar
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