AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not

2026-08-27

AI Summary

研究探讨了AI生成与编辑文本的风格特征差异，发现AI生成文本具有稳定的风格特征，而AI编辑则不然。通过分析熵、词汇多样性和密度等特征来区分AI生成和编辑的文本。

Abstract

Text generated by large language models (LLMs) has been shown to be stylometrically distinct from human-written text \citep{andreDetectingAIAuthorship2023, shahDetectingUnmaskingAIGenerated2023, oparaStyloAIDistinguishingAIGenerated2024, soto2024fewshot, liLinguisticDifferencesAI2025, selviogluFeatureExtractionAnalysis2025}. 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.

Problem

• stylometric footprint
• large language models
• AI generation
• AI editing
• lexical diversity

Innovation

• stylometric footprint
• entropy and lexical diversity
• AI editing vs. AI generation