@inproceedings{daghighfarsoodeh-etal-2026-peerify,
title = "Peerify: Benchmarking Peer-Review Claim Verification",
author = "Daghighfarsoodeh, Alireza and
Ebrahimi, Sajad and
Ghorbanpour, Ali and
Sadeghian, Soroush and
Cheraghi, Radin and
Arabzadeh, Negar and
Bagheri, Ebrahim",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.169/",
pages = "2865--2888",
ISBN = "979-8-89176-460-6",
abstract = "Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely manual and time-consuming process. We present Peerify, a pipeline for manuscript-grounded verification of peer-review claims. Given a manuscript and a review comment, Peerify pipeline decomposes reviews into atomic claims, retrieves relevant manuscript evidence, and determines whether each claim is supported by the paper. To support the development and evaluation of the pipeline, we construct a benchmark of 800 claims derived from authentic peer-review interactions collected from NeurIPS 2024 and ICLR 2024, including a 300-claim hand-labeled subset used to audit the automated supervision. We evaluate state-of-the-art language models and retrieval strategies within Peerify pipeline, together with entailment baselines. Our results demonstrate the importance of retrieval-centered verification and claim decomposition, while highlighting the challenges posed by ambiguous and interpretive reviewer claims. Automated labels agree with human consensus on 90.3{\%} of audited claims ($\kappa = 0.87$), while off-the-shelf entailment models stay below 0.24 macro-F1."
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<abstract>Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely manual and time-consuming process. We present Peerify, a pipeline for manuscript-grounded verification of peer-review claims. Given a manuscript and a review comment, Peerify pipeline decomposes reviews into atomic claims, retrieves relevant manuscript evidence, and determines whether each claim is supported by the paper. To support the development and evaluation of the pipeline, we construct a benchmark of 800 claims derived from authentic peer-review interactions collected from NeurIPS 2024 and ICLR 2024, including a 300-claim hand-labeled subset used to audit the automated supervision. We evaluate state-of-the-art language models and retrieval strategies within Peerify pipeline, together with entailment baselines. Our results demonstrate the importance of retrieval-centered verification and claim decomposition, while highlighting the challenges posed by ambiguous and interpretive reviewer claims. Automated labels agree with human consensus on 90.3% of audited claims (ąppa = 0.87), while off-the-shelf entailment models stay below 0.24 macro-F1.</abstract>
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%0 Conference Proceedings
%T Peerify: Benchmarking Peer-Review Claim Verification
%A Daghighfarsoodeh, Alireza
%A Ebrahimi, Sajad
%A Ghorbanpour, Ali
%A Sadeghian, Soroush
%A Cheraghi, Radin
%A Arabzadeh, Negar
%A Bagheri, Ebrahim
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F daghighfarsoodeh-etal-2026-peerify
%X Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely manual and time-consuming process. We present Peerify, a pipeline for manuscript-grounded verification of peer-review claims. Given a manuscript and a review comment, Peerify pipeline decomposes reviews into atomic claims, retrieves relevant manuscript evidence, and determines whether each claim is supported by the paper. To support the development and evaluation of the pipeline, we construct a benchmark of 800 claims derived from authentic peer-review interactions collected from NeurIPS 2024 and ICLR 2024, including a 300-claim hand-labeled subset used to audit the automated supervision. We evaluate state-of-the-art language models and retrieval strategies within Peerify pipeline, together with entailment baselines. Our results demonstrate the importance of retrieval-centered verification and claim decomposition, while highlighting the challenges posed by ambiguous and interpretive reviewer claims. Automated labels agree with human consensus on 90.3% of audited claims (ąppa = 0.87), while off-the-shelf entailment models stay below 0.24 macro-F1.
%U https://aclanthology.org/2026.emnlp-industry.169/
%P 2865-2888
Markdown (Informal)
[Peerify: Benchmarking Peer-Review Claim Verification](https://aclanthology.org/2026.emnlp-industry.169/) (Daghighfarsoodeh et al., EMNLP 2026)
ACL
- Alireza Daghighfarsoodeh, Sajad Ebrahimi, Ali Ghorbanpour, Soroush Sadeghian, Radin Cheraghi, Negar Arabzadeh, and Ebrahim Bagheri. 2026. Peerify: Benchmarking Peer-Review Claim Verification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2865–2888, Budapest, Hungary. Association for Computational Linguistics.