Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future
For researchers and practitioners building automated peer review systems, this survey provides a structured overview of current methods and challenges.
This survey synthesizes techniques for automating peer review stages using LLMs, covering generation, after-review tasks, and evaluation methods, while cataloging datasets and discussing limitations.
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.