@inproceedings{kumar-etal-2025-mixrevdetect,
    title = "{M}ix{R}ev{D}etect: Towards Detecting {AI}-Generated Content in Hybrid Peer Reviews.",
    author = "Kumar, Sandeep  and
      Garg, Samarth  and
      Sengupta, Sagnik  and
      Ghosal, Tirthankar  and
      Ekbal, Asif",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-short.79/",
    pages = "944--953",
    ISBN = "979-8-89176-190-2",
    abstract = "The growing use of large language models (LLMs) in academic peer review poses significant challenges, particularly in distinguishing AI-generated content from human-written feedback. This research addresses the problem of identifying AI-generated peer review comments, which are crucial to maintaining the integrity of scholarly evaluation. Prior research has primarily focused on generic AI-generated text detection or on estimating the fraction of peer reviews that may be AI-generated, often treating reviews as monolithic units. However, these methods fail to detect finer-grained AI-generated points within mixed-authorship reviews. To address this gap, we propose MixRevDetect, a novel method to identify AI-generated points in peer reviews. Our approach achieved an F1 score of 88.86{\%}, significantly outperforming existing AI text detection methods."
}