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This paper introduces ReviewFact, a benchmark for factual errors in peer review comments, and PremiseCheck, a pipeline where an LLM rewrites each comment into explicit premises that a cheaper decision model then verifies against the paper. The reported gains are cost and latency, with accuracy said to be comparable to GPT-5.5 on the authors' own benchmark. Readers should weigh these claims against the benchmark's construction, the paper's coverage of comments that bundle several implicit claims, and how well the results hold when comments are not checked in isolation.
PremiseCheck: Efficient Fact-Checking of Human and AI Peer Reviews
arXiv:2610.21046v1 Announce Type: cross Abstract: Peer review is under strain from the growth of submissions, and review comments written by humans or AI can misstate what a paper says. We introduce ReviewFact, a benchmark that labels such factual errors in review comments under one definition across NLP, machine learning, and the natural sciences, and that measures cost and latency alongside accuracy. A frontier LLM checks comments well but must read the whole paper for each check, while cheap decision models, which score whether a statement holds in a text, fall far short on whole comments that mix several implicit assertions. PremiseCheck is a framework that combines the two. An LLM rewrites each comment as explicit premises without reading the paper, and a decision model checks each premise against the paper. When each comment is checked on its own, PremiseCheck reaches accuracy comparable to GPT-5.5 across all three fields at about half its cost, and on NLP reviews at a quarter of its cost and less than half its latency.
Abstract:Peer review is under strain from the growth of submissions, and review comments written by humans or AI can misstate what a paper says. We introduce ReviewFact, a benchmark that labels such factual errors in review comments under one definition across NLP, machine learning, and the natural sciences, and that measures cost and latency alongside accuracy. A frontier LLM checks comments well but must read the whole paper for each check, while cheap decision models, which score whether a statement holds in a text, fall far short on whole comments that mix several implicit assertions. PremiseCheck is a framework that combines the two. An LLM rewrites each comment as explicit premises without reading the paper, and a decision model checks each premise against the paper. When each comment is checked on its own, PremiseCheck reaches accuracy comparable to GPT-5.5 across all three fields at about half its cost, and on NLP reviews at a quarter of its cost and less than half its latency.
29 pages, 19 figures, 23 tables. Code: this https URL
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2610.21046 [cs.CL]
(or arXiv:2610.21046v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.21046 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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