@inproceedings{wu-etal-2026-ai,
title = "Can {AI} Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future",
author = "Wu, Sihong and
Jiang, Owen and
Zhao, Yilun and
Hu, Tiansheng and
Ma, Yiling and
Zhang, Kaiyan and
Patwardhan, Manasi and
Cohan, Arman",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1504/",
pages = "32593--32619",
ISBN = "979-8-89176-390-6",
abstract = "Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances in large language models (LLMs) have motivated methods that assist or automate different stages of this pipeline. In this survey, we synthesize techniques for (i) peer review generation, including fine-tuning strategies, agent-based systems, RL-based methods, and emerging paradigms to enhance generation; (ii) after-review tasks including rebuttals, meta-review and revision aligned to reviews; and (iii) evaluation methods spanning human-centered, reference-based, LLM-based and aspect-oriented. We catalog datasets, compare modeling choices, and discuss limitations, ethical concerns, and future directions. The survey aims to provide practical guidance for building, evaluating, and integrating LLM systems across the full peer review workflow."
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%0 Conference Proceedings
%T Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
%A Wu, Sihong
%A Jiang, Owen
%A Zhao, Yilun
%A Hu, Tiansheng
%A Ma, Yiling
%A Zhang, Kaiyan
%A Patwardhan, Manasi
%A Cohan, Arman
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wu-etal-2026-ai
%X Peer review is a multi-stage process involving reviews, rebuttals, meta-reviews, final decisions, and subsequent manuscript revisions. Recent advances in large language models (LLMs) have motivated methods that assist or automate different stages of this pipeline. In this survey, we synthesize techniques for (i) peer review generation, including fine-tuning strategies, agent-based systems, RL-based methods, and emerging paradigms to enhance generation; (ii) after-review tasks including rebuttals, meta-review and revision aligned to reviews; and (iii) evaluation methods spanning human-centered, reference-based, LLM-based and aspect-oriented. We catalog datasets, compare modeling choices, and discuss limitations, ethical concerns, and future directions. The survey aims to provide practical guidance for building, evaluating, and integrating LLM systems across the full peer review workflow.
%U https://aclanthology.org/2026.acl-long.1504/
%P 32593-32619
Markdown (Informal)
[Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future](https://aclanthology.org/2026.acl-long.1504/) (Wu et al., ACL 2026)
ACL
- Sihong Wu, Owen Jiang, Yilun Zhao, Tiansheng Hu, Yiling Ma, Kaiyan Zhang, Manasi Patwardhan, and Arman Cohan. 2026. Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 32593–32619, San Diego, California, United States. Association for Computational Linguistics.