March 13, 2024, 4:42 a.m. | Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarlan

cs.LG updates on

arXiv:2403.07183v1 Announce Type: cross
Abstract: 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, …

abstract ai conference arxiv case case study chatgpt conference content at scale cs.lg expert generated impact language language model large language large language model likelihood llm monitoring peer reviews scale study text type

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