LLM辅助同行评审的位置：通过指导与反馈解决评审缺口
Position on LLM-Assisted Peer Review: Addressing Reviewer Gap through Mentoring and Feedback
AI总结：
本文提出通过LLM辅助指导和反馈系统解决评审缺口问题，旨在提升评审质量并促进学术生态可持续发展。
摘要：
人工智能研究的迅速发展加剧了评审缺口，威胁着同行评审的可持续性，并加剧了低质量评估的循环。本文立场论文批评现有LLM自动生成评审的方法，并主张一种范式转变，将LLM定位为协助和教育人类评审员的工具。我们定义高质量同行评审的核心原则，并提出两个互补的系统，基于这些基础：(i) 一个LLM辅助指导系统，培养评审员的长期能力；(ii) 一个LLM辅助反馈系统，帮助评审员提升评审质量。这种以人类为中心的方法旨在增强评审员的专业能力，并为构建更加可持续的学术生态系统做出贡献。
英文摘要：
The rapid expansion of AI research has intensified the Reviewer Gap, threatening the peer-review sustainability and perpetuating a cycle of low-quality evaluations. This position paper critiques existing LLM approaches that automatically generate reviews and argues for a paradigm shift that positions LLMs as tools for assisting and educating human reviewers. We define the core principles of high-quality peer review and propose two complementary systems grounded in these foundations: (i) an LLM-assisted mentoring system that cultivates reviewers' long-term competencies, and (ii) an LLM-assisted feedback system that helps reviewers refine the quality of their reviews. This human-centered approach aims to strengthen reviewer expertise and contribute to building a more sustainable scholarly ecosystem.
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。
