@inproceedings{ou-etal-2025-claimcheck,
    title = "{CLAIMCHECK}: How Grounded are {LLM} Critiques of Scientific Papers?",
    author = "Ou, Jiefu  and
      Walden, William Gantt  and
      Sanders, Kate  and
      Jiang, Zhengping  and
      Sun, Kaiser  and
      Cheng, Jeffrey  and
      Jurayj, William  and
      Wanner, Miriam  and
      Liang, Shaobo  and
      Morgan, Candice  and
      Han, Seunghoon  and
      Wang, Weiqi  and
      May, Chandler  and
      Recknor, Hannah  and
      Khashabi, Daniel  and
      Van Durme, Benjamin",
    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.1185/",
    doi = "10.18653/v1/2025.findings-emnlp.1185",
    pages = "21712--21735",
    ISBN = "979-8-89176-335-7",
    abstract = "A core part of scientific peer review involves providing expert critiques that directly assess the scientific claims a paper makes. While it is now possible to automatically generate plausible (if generic) reviews, ensuring that these reviews are sound and grounded in the papers' claims remains challenging. To facilitate LLM benchmarking on these challenges, we introduce CLAIMCHECK, an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews mined from OpenReview. CLAIMCHECK is richly annotated by ML experts for weakness statements in the reviews and the paper claims that they dispute, as well as fine-grained labels of the validity, objectivity, and type of the identified weaknesses. We benchmark several LLMs on three claim-centric tasks supported by CLAIMCHECK, requiring models to (1) associate weaknesses with the claims they dispute, (2) predict fine-grained labels for weaknesses and rewrite the weaknesses to enhance their specificity, and (3) verify a paper{'}s claims with grounded reasoning. Our experiments reveal that cutting-edge LLMs, while capable of predicting weakness labels in (2), continue to underperform relative to human experts on all other tasks."
}