分析ICLR审稿流程，揭示回复如何影响评分结果。
Insights from the ICLR Peer Review and Rebuttal Process
- 通过量化评分与作者回复互动，识别审稿关键影响因素。
- 初评分和合审稿人意见是评分变化最强预测因子。
- 为作者提供有效回复策略，助改进审稿公平性与效率。
同行评审是机器学习顶级会议如ICLR的核心环节。随着投稿量增加，理解评审过程的性质与动态对提升效率、效果及论文质量至关重要。本文对ICLR 2024与2025年审稿过程进行大规模分析，聚焦评审前后评分变化及作者-审稿人互动。研究涵盖评分分布、作者参与度、审稿时间模式以及合审稿人影响力。结合量化分析与大模型（LLM）对评审文本和回复内容的分类，我们识别出各评分组的常见优劣势，以及最可能引发评分变动的回复策略。结果表明，初始评分与合审稿人意见是评分变化最强预测因子，显示审稿人之间存在一定影响。对于临界论文，有策略的作者回应可显著改变审稿人看法。本研究为优化同行评审提供实证依据，指导作者撰写高效回复，并助力社区构建更公平、高效的评审机制。代码与评分变化数据已开源：https://github.com/papercopilot/iclr-insights。
原文摘要 · Abstract (English)
Peer review is a cornerstone of scientific publishing, including at premier machine learning conferences such as ICLR. As submission volumes increase, understanding the nature and dynamics of the review process is crucial for improving its efficiency, effectiveness, and the quality of published papers. We present a large-scale analysis of the ICLR 2024 and 2025 peer review processes, focusing on before- and after-rebuttal scores and reviewer-author interactions. We examine review scores, author-reviewer engagement, temporal patterns in review submissions, and co-reviewer influence effects. Combining quantitative analyses with LLM-based categorization of review texts and rebuttal discussions, we identify common strengths and weaknesses for each rating group, as well as trends in rebuttal strategies that are most strongly associated with score changes. Our findings show that initial scores and the ratings of co-reviewers are the strongest predictors of score changes during the rebuttal, pointing to a degree of reviewer influence. Rebuttals play a valuable role in improving outcomes for borderline papers, where thoughtful author responses can meaningfully shift reviewer perspectives. More broadly, our study offers evidence-based insights to improve the peer review process, guiding authors on effective rebuttal strategies and helping the community design fairer and more efficient review processes. Our code and score changes data are available at https://github.com/papercopilot/iclr-insights.
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