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    <abstract>Text generated by large language models (LLMs) has been shown to be stylometrically distinct from human-written text (CITATION). But LLMs are increasingly used not only to generate text but also to edit human writing, and it is unclear whether the two leave the same trace. We show that AI generation leaves a consistent “stylometric footprint”: a small subset of features, primarily entropy and lexical diversity, consistently separates AI-generated text from human writing across 8 LLMs and 5 domains, while the remaining features depend heavily on the domain and generator. AI editing, however, does not reproduce the same footprint. Relative to their human-written sources, AI-edited texts show only a small increase in lexical diversity and a decrease in entropy, rather than the joint increase that characterizes AI generation. Lexical density, which contributes little to generation, instead becomes the dominant editing-associated signal. Stylometric features therefore separate AI-edited text from AI-generated text but are substantially less effective at separating it from human-written text. Our results suggest that “AI text” is not a single phenomenon: generation and editing leave qualitatively different stylometric traces and should be studied separately.</abstract>
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%0 Conference Proceedings
%T AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not
%A Shan, Zhengyang
%A Lee, Yukyung
%A Hao, Sophie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F shan-etal-2026-ai
%X Text generated by large language models (LLMs) has been shown to be stylometrically distinct from human-written text (CITATION). But LLMs are increasingly used not only to generate text but also to edit human writing, and it is unclear whether the two leave the same trace. We show that AI generation leaves a consistent “stylometric footprint”: a small subset of features, primarily entropy and lexical diversity, consistently separates AI-generated text from human writing across 8 LLMs and 5 domains, while the remaining features depend heavily on the domain and generator. AI editing, however, does not reproduce the same footprint. Relative to their human-written sources, AI-edited texts show only a small increase in lexical diversity and a decrease in entropy, rather than the joint increase that characterizes AI generation. Lexical density, which contributes little to generation, instead becomes the dominant editing-associated signal. Stylometric features therefore separate AI-edited text from AI-generated text but are substantially less effective at separating it from human-written text. Our results suggest that “AI text” is not a single phenomenon: generation and editing leave qualitatively different stylometric traces and should be studied separately.
%U https://aclanthology.org/2026.emnlp-main.1725/
%P 39352-39376

Markdown (Informal)

[AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not](https://aclanthology.org/2026.emnlp-main.1725/) (Shan et al., EMNLP 2026)

• AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not (Shan et al., EMNLP 2026)

ACL

• Zhengyang Shan, Yukyung Lee, and Sophie Hao. 2026. AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 39352–39376, Budapest, Hungary. Association for Computational Linguistics.