Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866910184715059200 |
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| author | Wu, Sihong Jiang, Owen Zhao, Yilun Hu, Tiansheng Ma, Yiling Zhang, Kaiyan Patwardhan, Manasi Cohan, Arman |
| author_facet | Wu, Sihong Jiang, Owen Zhao, Yilun Hu, Tiansheng Ma, Yiling Zhang, Kaiyan Patwardhan, Manasi Cohan, Arman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_27924 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future Wu, Sihong Jiang, Owen Zhao, Yilun Hu, Tiansheng Ma, Yiling Zhang, Kaiyan Patwardhan, Manasi Cohan, Arman Computation and Language Artificial Intelligence 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. |
| title | Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.27924 |