CLAIMCHECK : How Grounded are LLM Critiques of Scientific Papers? (2025)

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.

## introduction scientific peer review demands expert critique of the claims that a paper makes-about results, theorems, approaches, novelty, etc. it is thus paramount that these critiques be grounded in the actual assertions made by a papers' authors, a principle reflected in the guidelines for numerous top-tier ai conferences ( however, collecting authentic data for grounded peer review is challenging. existing work tends to simplify this challenge by narrowing either the scope of the claims or the evidence pool @xcite , making systems trained on such data difficult to deploy in real-world peer review scenarios. further, these approaches tend to employ binary claim factuality judgments, limiting their applicability to the common case in which a claim is flawed but not entirely false @xcite . to address these challenges, we introduce claimcheck, a novel resource of dataset and tasks that supports scientific claim verification and claim-grounded assessment of (automatic) peer review. claimcheck is a high-quality, multimodal collection of rejected neurips submissions and their reviews, annotated by ml experts for rich information about reviewer-identified, claim-related weaknesses, with links to the paper claims they dispute (figure 1 ). to our knowledge, claimcheck is the first resource that supports both scientific claim verification and claim-grounded peer review. claims are sourced directly from papers' full texts, rather than synthetically constructed @xcite . further, as these claims are rarely entirely true or entirely false, we develop an informative, multi-label ontology for diverse weakness types a claim may exhibit, informed by a pilot study with domain experts. we leverage claimcheck to benchmark cutting-edge multimodal and reasoning llms (gpt-4o, gemini-2.0-flash, o3-mini, o1) on a suite of claim-centric reviewing tasks, including (1) associating reviewer-identified weaknesses with the paper claims they dispute; (2) predicting fine-grained weakness types for claims and refining reviewer-written weaknesses for enhanced specificity; and (3) verifying claims from scratch using grounded reasoning. through extensive experiments, we find that even frontier models exhibit significant limitations (except for the label prediction sub-task in (2)) as scientific claim verifiers and reviewing assistants. to summarize our contributions, we: in focusing on grounding reviewer weaknesses in targeted claims, we follow several prior works that emphasize the dialectic nature of peer review, in which authors and reviewers respond directly to one another. @xcite introduce the rr (review-rebuttal or ape) dataset for mining arguments from reviews and rebuttals of iclr submissions.the aries dataset from d' @xcite features reviewer comments from submissions to several computer science conferences, automatically aligned to paper edits made in response. @xcite analyzes the substantiation in peer reviews by verifying claims in these reviews, and @xcite formulates an intertextual model of papers with their peer reviews. claim verification weaknesses identified by reviewers can be understood as verifying the claims that they target, and claim (or fact) verification is its own active research program. historically, datasets and shared tasks for claim verification, such as fever @xcite , sciver (wadden and @xcite , covid-fact @xcite and averitec @xcite , have tended to emphasize prediction of scalar veracity judgments over written explanations (as weaknesses provide; @xcite , although a number of more recent works have given more attention to the latter @xcite @xcite . several other claim verification datasets focus on scientific domains. notable examples include sci-fact @xcite , which features 1.4k expert-written scientific claims from a variety of fields; scifact-open @xcite , which builds on scifact, with an additional 279 claims from similarly diverse areas; and scitab @xcite , which provides a set of 1.2k claims describing table results extracted from arxiv papers on computer science, requiring compositional reasoning on tables for their verification. we refer the reader to @xcite for a general overview of claim verification. our work vs. prior work while claim-check draws raw data from similar sources as other works on automated peer review(viz. neurips openreview submissions), it is unique in focusing on the relationship between reviewer-identified weaknesses and papers' claims. within the claim verification literature, our work is distinctive in drawing evidence for disputed claims from reviews and in leveraging complete paper data (text, images, figures, algorithms, captions)-both from the reviewed paper and from related works-for verification. the most similar work to claimcheck is @xcite , while this work focuses on verifying claims in the review based on evidenece in the review, in contrast, claimcheck aims to verify claims in the paper using weaknesses in the review as reference and evidence in the paper and related work. the suite of tasks enabled by claimcheck that we explore in §4 are novel for both domains surveyed above. for automated peer review, these tasks introduce a novel claim-centric focus to review generation and evaluation, which could be viewed as a specific realization of the intertextual relationship in @xcite . while @xcite tries to establish the general intertextual relationship between every sentences in the review with different parts of the paper, we investigate a more specific type of such relationships, i.e. weaknesses comments in the review and the corresponding claims in the paper. moreover, we conduct a deeper analysis on this relationship to devise a label taxonomy of finegrained claim weakness categories and corresponding claim-grounded review tasks. for claim verification, these tasks provide a more realistic setting for verification of "in-the-wild" claims taken from real papers, leveraging fine-grained weakness types rather than binary veracity labels. ## claimcheck construction given a paper and one of its reviews, we aim to collect pairs consisting of (1) a claim-related weakness and (2) one or more target claims. we define a claim-related weakness as a contiguous passage from the review that disputes the validity of one or more claims that the paper makes. 2 for each weakness we also collect a detailed set of labels. we first describe our data preprocessing pipeline ( §3.1), followed by our annotation tasks ( §3.2) and the actual annotation process ( §3.3). ## experiments to support progress on claim-grounded review with llms, our experiments benchmark a set of cutting-edge multimodal and reasoning models (gpt-4o, gemini-2.0-flash, o3-mini, o1) in the zero-shot setting on three sets of experiments that leverage claimcheck and that are motivated by specific use cases (described below): claim association (ca), weakness labeling and editing (wle), and claim verification (cv). 8 we use the pilot and main annotation sets to evaluate the ca tasks and the label prediction of the wle task, and use main data only for other tasks. ## conclusion this work has introduced claimcheck-a benchmark of reviewer-identified weaknesses in neurips 2023 and 2024 submissions, richly annotated with descriptive labels by experts and grounded in the claims that they dispute in the reviewed papers. further, we benchmark various llms on three novel tasks enabled by claim-check-weakness labeling and editing (wle), claim association (ca), and claim verification (cv)-all aimed at assisting reviewers during the peer review process. across these tasks, we find even cutting-edge llms struggle to provide specific, grounded reviews and to identify and verify the specific claims targeted by those reviews. we release claimcheck to support further research on claim-grounded automated peer review.