# A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

Source: https://arxiv.org/abs/2403.07183
Kiwi News: https://news.kiwistand.com/stories/A-Case-Study-on-the-Impact-of-ChatGPT-on-AI-Conference-Peer-Reviews?index=0x661fc665330494e62cdda7a92f1bace04ccc4121aad23f5f1a03bd8074605f91117149f0
Submitted by misha on 2024-04-17 UTC, 1 upvote, 0 comments

## Summary

A study by Weixin Liang, Zachary Izzo, and colleagues estimates that between 6.5% and 16.9% of peer review text submitted to four AI conferences (ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023) could have been substantially modified by large language models. The authors use a maximum likelihood model that relies on expert-written and AI-generated reference texts to measure LLM use at the corpus level. The estimated share of LLM-generated text is higher in reviews reporting lower confidence, submitted close to the deadline, and written by reviewers less likely to respond to author rebuttals.

## Article

Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

Authors: Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.