@inproceedings{wu-etal-2026-ai,
    title = "Can {AI} Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future",
    author = "Wu, Sihong  and
      Jiang, Owen  and
      Zhao, Yilun  and
      Hu, Tiansheng  and
      Ma, Yiling  and
      Zhang, Kaiyan  and
      Patwardhan, Manasi  and
      Cohan, Arman",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1504/",
    pages = "32593--32619",
    ISBN = "979-8-89176-390-6",
    abstract = "Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances in large language models (LLMs) have motivated methods that assist or automate different stages of this pipeline. In this survey, we synthesize techniques for (i) peer review generation, including fine-tuning strategies, agent-based systems, RL-based methods, and emerging paradigms to enhance generation; (ii) after-review tasks including rebuttals, meta-review and revision aligned to reviews; and (iii) evaluation methods spanning human-centered, reference-based, LLM-based and aspect-oriented. We catalog datasets, compare modeling choices, and discuss limitations, ethical concerns, and future directions. The survey aims to provide practical guidance for building, evaluating, and integrating LLM systems across the full peer review workflow."
}