@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?
%A Ou, Jiefu
%A Walden, William Gantt
%A Sanders, Kate
%A Jiang, Zhengping
%A Sun, Kaiser
%A Cheng, Jeffrey
%A Jurayj, William
%A Wanner, Miriam
%A Liang, Shaobo
%A Morgan, Candice
%A Han, Seunghoon
%A Wang, Weiqi
%A May, Chandler
%A Recknor, Hannah
%A Khashabi, Daniel
%A Van Durme, Benjamin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F ou-etal-2025-claimcheck
%X 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.
%R 10.18653/v1/2025.findings-emnlp.1185
%U https://aclanthology.org/2025.findings-emnlp.1185/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1185
%P 21712-21735
Markdown (Informal)
[CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?](https://aclanthology.org/2025.findings-emnlp.1185/) (Ou et al., Findings 2025)
ACL
- Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, and Benjamin Van Durme. 2025. CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 21712–21735, Suzhou, China. Association for Computational Linguistics.