# GPT Editors, Not Authors: The Stylistic Footprint of LLMs in Academic Preprints

arXiv: https://arxiv.org/abs/2505.17327 · HTML: https://mlsanity.com/paper/2505.17327/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 18 of 18)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews](https://mlsanity.com/paper/2403.07183/) (2024)
- [Mapping the Increasing Use of LLMs in Scientific Papers](https://mlsanity.com/paper/2404.01268/) (2024)
- [Delving into LLM-assisted writing in biomedical publications through excess vocabulary](https://mlsanity.com/paper/2406.07016/) (2024)
- [The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates](https://mlsanity.com/paper/2405.02150/) (2024)
- [TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization](https://mlsanity.com/paper/2402.13249/) (2024)
- [FABLES: Evaluating faithfulness and content selection in book-length summarization](https://mlsanity.com/paper/2404.01261/) (2024)
- [Large Language Models can be Guided to Evade AI-Generated Text Detection](https://mlsanity.com/paper/2305.10847/) (2023)
- [ChatGPT "contamination": estimating the prevalence of LLMs in the scholarly literature](https://mlsanity.com/paper/2403.16887/) (2024)
- [How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts](https://mlsanity.com/paper/2310.05095/) (2023)
- [Techniques for supercharging academic writing with generative AI](https://mlsanity.com/paper/2310.17143/) (2023)
- [Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection](https://mlsanity.com/paper/2406.06558/) (2024)

## Built on by (most cited first, 2 of 2)

- [Does Scientific Writing Converge to U.S. English? Evidence from Generative AI-Assisted Publications](https://mlsanity.com/paper/2511.11687/) (2025)
- [The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences](https://mlsanity.com/paper/2509.11295/) (2025)

## Abstract (arXiv)

The proliferation of Large Language Models (LLMs) in late 2022 has impacted academic writing, threatening credibility, and causing institutional uncertainty. We seek to determine the degree to which LLMs are used to generate critical text as opposed to being used for editing, such as checking for grammar errors or inappropriate phrasing. In our study, we analyze arXiv papers for stylistic segmentation, which we measure by varying a PELT threshold against a Bayesian classifier trained on GPT-regenerated text. We find that LLM-attributed language is not predictive of stylistic segmentation, suggesting that when authors use LLMs, they do so uniformly, reducing the risk of hallucinations being introduced into academic preprints.