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

TL;DR

This paper advocates for treating OpenReview as a vital community resource to improve peer review, create meaningful benchmarks, and support alignment research in the context of large language models.

Contribution

It proposes leveraging OpenReview as a shared asset to enhance peer review, develop open-ended benchmarks, and facilitate alignment research involving expert interactions.

Findings

OpenReview can improve peer review quality and accountability.

It enables the creation of genuine, expert-rooted benchmarks.

Supports alignment research through real-world expert interactions.

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 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.