OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of LLMs

Hao Sun, Yunyi Shen, Mihaela van der Schaar

Keywords: Large Language Model Alignment, Reasoning, Peer Review, Open-Ended Evaluation, Data-Centric AI

TL;DR: OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of LLMs

Abstract: 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 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.

Submission Number: 325