将OpenReview作为LLM时代的研究核心资产，推动评审与对齐研究
OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models
- 把开放评审数据作为高质量专家知识库使用
- 可提升评审质量、构建真实专家对话的评测基准
- 适合关注学术伦理与模型对齐的研究者
在大语言模型时代，高质量、领域丰富且持续演化的数据集，承载着专家知识、核心人类价值观和推理能力，愈发珍贵。本文主张，持续演进的OpenReview（包含论文、同行评审、作者回应、元评审及决策结果）应被更广泛地视为社区核心资产，以推动大模型时代的研究进展。我们指出三个独特贡献方向：提升评审流程的质量、可扩展性与问责性；基于真实专家讨论建立有意义的开放式评测基准；通过反映专家评估、意图与科学价值观的现实交互，支持对齐研究。为实现这些潜力，建议社区共同探索针对OpenReview的标准化评测与使用规范，推动关于负责任数据使用、伦理考量与集体维护的广泛对话。
原文摘要 · Abstract (English)
In the era of large language models (LLMs), high-quality, domain-rich, and continuously evolving datasets capturing expert-level knowledge, core human values, and reasoning are increasingly valuable. This position paper argues that OpenReview -- the continually evolving repository of research papers, peer reviews, author rebuttals, meta-reviews, and decision outcomes -- should be leveraged more broadly as a core community asset for advancing research in the era of LLMs. We highlight three promising areas in which OpenReview can uniquely contribute: enhancing the quality, scalability, and accountability of peer review processes; enabling meaningful, open-ended benchmarks rooted in genuine expert deliberation; and supporting alignment research through real-world interactions reflecting expert assessment, intentions, and scientific values. To better realize these opportunities, we suggest the community collaboratively explore standardized benchmarks and usage guidelines around OpenReview, inviting broader dialogue on responsible data use, ethical considerations, and collective stewardship.
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