arXiv:2601. 17363v3 Announce Type: replace-cross Abstract: This study investigates whether readers prefer AI-generated short stories in Italian over one written by a renowned Italian author.
By Michael Farrell
The study examines how professional English editing influences AI text detectors’ false-positive rates for non-native academic writing. Using 135,389 pairs of original and edited manuscripts, researchers found that detector responses varied widely—some editors increased AI scores while others decreased them—and that score changes correlated with the extent of editing. These results highlight professional editing style as a key confounding factor in AI detection, complicating the distinction between AI authorship and linguistic style.
By Hyeonchu Park, Gahye Jeong, Bugeun Kim
The study demonstrates that text produced by large language models (LLMs) leaves a distinct stylometric footprint—primarily increased entropy and lexical diversity—across multiple models and domains. In contrast, AI editing of human text does not replicate this footprint; edited texts show only modest lexical diversity gains and reduced entropy, with lexical density emerging as the key distinguishing feature. Consequently, stylometric analysis can differentiate AI-generated from AI-edited content, but is less effective at distinguishing either from purely human writing.
By Zhengyang Shan, Yukyung Lee, Sophie Hao
arXiv:2606.26040v2 Announce Type: replace
Abstract: AI translation of literary works is increasingly common. While the content may be rendered adequately, we do not know enough about how readers expe...
By Yves Ferstler, Adam Podoxin, Ty Brassington, Ga\"elle Laperri\`ere, Roman Grundkiewicz, Marie-Jean Meurs, Maite Taboada, Marzena Karpinska
arXiv:2510.08831v2 Announce Type: replace
Abstract: As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standar...
By Wouter Haverals, Meredith Martin
arXiv:2608. 12630v1 Announce Type: cross Abstract: While large language models can generate entire novels, there is little information about the level of formal variation in their output over many generations.
By Mehdy Sedaghat Payam, Justin Quinn