Rhythm uniformity and burstiness
Parent: LLM rhetorical-pattern tells in prose · Published reference · snapshot 2026-09-23
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Source-anchored research for Rhythm uniformity and burstiness. Run status: COMPLETED.
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Definitions
- Burstiness is defined as how much writing patterns and perplexities vary over a whole document; the claim is that humans vary naturally while LLMs choose next words by the same rule and so show low burstiness. (medium confidence) [source]
Facts and statements
- Across six LLMs from three families, human news text shows more scattered sentence-length distributions, more varied vocabulary and dependency types than LLM text; differences between humans and LLMs exceed differences between LLMs. (low confidence) [source]
- GPTZero stopped relying on perplexity and burstiness as primary detection signals in autumn 2023, moving to a deep-learning classifier; the stats remain one of seven indicators. (medium confidence) (verified as of 2026-09-23) [source]
- Perplexity/burstiness are relative to a specific scoring model, so results shift with each model release; closed models expose no token probabilities, forcing open-model proxies. (low confidence) [source]
- Text heavily present in training data (e.g. the Declaration of Independence) scores as low perplexity and is misflagged as AI; non-native English writing also scores low perplexity/burstiness (2023 Stanford TOEFL study), so the metric produces false positives. (low confidence) [source]
Children
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