Literary Non-Style in LLM-Generated Text

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.

Computation and Language (cs.CL)

arXiv:2607.17228 [cs.CL]

(or arXiv:2607.17228v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.17228 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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