Unverified50% confidenceFactExact time
Perplexity depends heavily on the tokenizer: different tokenizers split text into different token granularities, so the same passage yields different token counts, and perplexity values cannot be compared directly.
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50%
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Long-term
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10/17/2026
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Related Claims
VerifiedText 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.76% similarUnverifiedApplying synonym substitution or sentence restructuring to text significantly alters the perplexity distribution, causing perplexity-based detection to fail.74% similarUnverifiedFor languages with complex morphology or difficult word segmentation, the Character Error Rate (CER) is sometimes more robust than WER because it bypasses word segmentation ambiguity, but CER similarly cannot distinguish the severity of errors.70% similarUnverified传统并行草拟机制存在「后缀衰减」问题，批量预判的字符无法同步适配实时更新的语境，可能拼接出不通顺的错误文本68% similarUnverifiedMethods such as Word2Vec and GloVe produce static embeddings, where the vector for a given word is the same regardless of context, leading to ambiguity problems.68% similar
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