用AI助手缓解论文评审压力，提升质量与效率。
Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem
- 用大语言模型辅助审稿人、作者和领域主席，不取代人类判断。
- 提出可验证事实、提升审稿表现、改进论文质量的具体应用方向。
- 适合关注学术评审系统改革的学者与会议组织者。
机器学习领域顶级会议如NeurIPS、ICML和ICLR的论文投稿量呈指数增长，远超合格审稿人的有限能力，导致评审质量下降、标准不一及审稿疲劳。本文主张将人工智能辅助评审作为紧急的研究与基础设施优先事项。我们提出构建一个全面的AI增强型评审生态，利用大语言模型（LLMs）作为作者、审稿人和区域主席（ACs）的智能协作工具，而非替代品。具体包括：支持事实核查、指导审稿人表现、帮助作者优化论文、协助区域主席决策。关键在于需获取更细粒度、结构化且合乎伦理的评审数据以推动系统发展。文中还提出了研究议程与实验示例，并讨论了关键技术与伦理挑战。呼吁整个机器学习社区主动建设这一未来，保障科学验证的完整性与可扩展性，同时维持高水准的同行评审。
原文摘要 · Abstract (English)
Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale. Exponential growth in manuscript submissions to premier ML venues such as NeurIPS, ICML, and ICLR is outpacing the finite capacity of qualified reviewers, leading to concerns about review quality, consistency, and reviewer fatigue. This position paper argues that AI-assisted peer review must become an urgent research and infrastructure priority. We advocate for a comprehensive AI-augmented ecosystem, leveraging Large Language Models (LLMs) not as replacements for human judgment, but as sophisticated collaborators for authors, reviewers, and Area Chairs (ACs). We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making. Crucially, we contend that the development of such systems hinges on access to more granular, structured, and ethically-sourced peer review process data. We outline a research agenda, including illustrative experiments, to develop and validate these AI assistants, and discuss significant technical and ethical challenges. We call upon the ML community to proactively build this AI-assisted future, ensuring the continued integrity and scalability of scientific validation, while maintaining high standards of peer review.
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