Literary Non-Style in LLM-Generated Text

Cory Massaro
7/19/2026
cs.CL

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Abstract

Prior work on LLM-generated text has demonstrated quantitative and qualitative departures from text produced by humans. LLM-generated texts differ from human writing in style, resulting in a characteristic textual "feel," while the semantic range of LLMs is much restricted compared to that of humans. In this contribution, I note simple but consistent patterns in the statistical distribution of n-grams within LLM-generated text. Via qualitative analysis of these n-grams, I reveal deficiencies in LLM style. Because higher-order n-grams correlate to semantic content, I conclude that questions of style and semantics are not cleanly separable.

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Cite this paper

@article{massaro2026literary,
  title  = {Literary Non-Style in LLM-Generated Text},
  author = {Cory Massaro},
  year   = {2026},
  eprint = {2607.17228},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.17228v1}
}

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