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