@inproceedings{garg-etal-2025-revieweval,
    title = "{R}eview{E}val: An Evaluation Framework for {AI}-Generated Reviews",
    author = "Garg, Madhav Krishan  and
      Prasad, Tejash  and
      Singhal, Tanmay  and
      Kirtani, Chhavi  and
      Mandal, Murari  and
      Kumar, Dhruv",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1120/",
    doi = "10.18653/v1/2025.findings-emnlp.1120",
    pages = "20542--20564",
    ISBN = "979-8-89176-335-7",
    abstract = "The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: (1) ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and (2) ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78{\%} and 47.62{\%} over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97{\%} and 12.73{\%}, enhances adherence to guidelines by 10.11{\%} and 47.26{\%} respectively. This paper establishes essential metrics for AI-based peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research."
}