EchoReview: 从科学引文的回声中学习同行评审
EchoReview: Learning Peer Review from the Echoes of Scientific Citations
AI总结：
EchoReview通过引用上下文驱动的数据合成方法，构建了首个大规模跨会议、跨年度的评审数据集，并训练出能提升核心评审维度的自动化评审员。
摘要：
随着科学提交量的快速增长，传统同行评审系统正面临前所未有的可扩展性压力，凸显了急需一种既可扩展又可靠的自动化评审方法的迫切需求。现有的基于真实评审数据的监督微调方法本质上受到单一数据源的限制，以及人类评审的固有主观性和不一致性的影响，限制了其支持高质量自动化评审的能力。为了解决这些问题，我们提出了EchoReview，一种基于引用上下文的数据合成框架，系统地挖掘学术引用中的隐含集体评价信号，并将科学社区的长期判断转化为结构化的评审数据。基于此流程，我们构建了EchoReview-16K，首个大规模、跨会议、跨年度的引用驱动评审数据集，并训练了一个自动化评审员EchoReviewer-7B。实验结果表明，EchoReviewer-7B在证据支持和评审全面性等核心评审维度上能够实现显著且稳定的提升，验证了引用上下文作为可靠自动化同行评审数据范式的有效性。
英文摘要：
As the volume of scientific submissions continues to grow rapidly, traditional peer review systems are facing unprecedented scalability pressures, highlighting the urgent need for automated reviewing methods that are both scalable and reliable. Existing supervised fine-tuning approaches based on real review data are fundamentally constrained by single-source of data as well as the inherent subjectivity and inconsistency of human reviews, limiting their ability to support high-quality automated reviewers. To address these issues, we propose EchoReview, a citation-context-driven data synthesis framework that systematically mines implicit collective evaluative signals from academic citations and transforms scientific community's long-term judgments into structured review-style data. Based on this pipeline, we construct EchoReview-16K, the first large-scale, cross-conference, and cross-year citation-driven review dataset, and train an automated reviewer, EchoReviewer-7B. Experimental results demonstrate that EchoReviewer-7B can achieve significant and stable improvements on core review dimensions such as evidence support and review comprehensiveness, validating citation context as a robust and effective data paradigm for reliable automated peer review.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
