Literary Non-Style in LLM-Generated Text
Computation and Language
2026-07-19 v1
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.
Cite
@article{arxiv.2607.17228,
title = {Literary Non-Style in LLM-Generated Text},
author = {Cory Massaro},
journal= {arXiv preprint arXiv:2607.17228},
year = {2026}
}