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

• URL: http://arxiv.org/abs/2505.21537v1
• Date: Sat, 24 May 2025 09:07:13 GMT
• Title: OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models
• Authors: Hao Sun, Yunyi Shen, Mihaela van der Schaar,
• Abstract summary: OpenReview is a continually evolving repository of research papers, peer reviews, author rebuttals, meta-reviews, and decision outcomes.<n>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.<n>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.
• Score: 55.21589313404023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem [87.30601926271864]
  InnoEval is a deep innovation evaluation framework designed to emulate human-level idea assessment.<n>We apply a heterogeneous deep knowledge search engine that retrieves and grounds dynamic evidence from diverse online sources.<n>We construct comprehensive datasets derived from authoritative peer-reviewed submissions to benchmark InnoEval.
  arXiv  Detail & Related papers  (2026-02-16T00:40:31Z)
• Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review [53.99984738447279]
  Recent work frames this task as automatic text generation, underusing author expertise and intent.<n>We introduce REspGen, a generation framework that integrates explicit author input, multi-attribute control, and evaluation-guided refinement.<n>To support this formulation, we construct Re$3$Align, the first large-scale dataset of aligned review-response--revision triplets.
  arXiv  Detail & Related papers  (2026-01-19T14:07:10Z)
• CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection [60.52240468810558]
  We introduce CoCoNUTS, a content-oriented benchmark built upon a fine-grained dataset of AI-generated peer reviews.<n>We also develop CoCoDet, an AI review detector via a multi-task learning framework, to achieve more accurate and robust detection of AI involvement in review content.
  arXiv  Detail & Related papers  (2025-08-28T06:03:11Z)
• Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback [81.0031690510116]
  We present a structured approach for automated novelty evaluation that models expert reviewer behavior through three stages.<n>Our method is informed by a large scale analysis of human written novelty reviews.<n> Evaluated on 182 ICLR 2025 submissions, the approach achieves 86.5% alignment with human reasoning and 75.3% agreement on novelty conclusions.
  arXiv  Detail & Related papers  (2025-08-14T16:18:37Z)
• Expert Preference-based Evaluation of Automated Related Work Generation [54.29459509574242]
  We propose GREP, a multi-turn evaluation framework that integrates classical related work evaluation criteria with expert-specific preferences.<n>For better accessibility, we design two variants of GREP: a more precise variant with proprietary LLMs as evaluators, and a cheaper alternative with open-weight LLMs.
  arXiv  Detail & Related papers  (2025-08-11T13:08:07Z)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• Identifying Aspects in Peer Reviews [61.374437855024844]
  We develop a data-driven schema for deriving fine-grained aspects from a corpus of peer reviews.<n>We introduce a dataset of peer reviews augmented with aspects and show how it can be used for community-level review analysis.
  arXiv  Detail & Related papers  (2025-04-09T14:14:42Z)
• Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review [3.802113616844045]
  This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality.<n>We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation.
  arXiv  Detail & Related papers  (2025-01-22T17:00:27Z)
• DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation [11.010557279355885]
  This study empirically analyzes benchmark comments using a novel set of criteria informed by prior research and developer interviews.<n>Our evaluation framework, DeepCRCEval, integrates human evaluators and Large Language Models (LLMs) for a comprehensive reassessment of current techniques.
  arXiv  Detail & Related papers  (2024-12-24T08:53:54Z)
• Generative Adversarial Reviews: When LLMs Become the Critic [1.2430809884830318]
  We introduce Generative Agent Reviewers (GAR), leveraging LLM-empowered agents to simulate faithful peer reviewers.<n>Central to this approach is a graph-based representation of manuscripts, condensing content and logically organizing information.<n>Our experiments demonstrate that GAR performs comparably to human reviewers in providing detailed feedback and predicting paper outcomes.
  arXiv  Detail & Related papers  (2024-12-09T06:58:17Z)
• LocalValueBench: A Collaboratively Built and Extensible Benchmark for Evaluating Localized Value Alignment and Ethical Safety in Large Language Models [0.0]
  The proliferation of large language models (LLMs) requires robust evaluation of their alignment with local values and ethical standards. textscLocalValueBench is a benchmark designed to assess LLMs' adherence to Australian values.
  arXiv  Detail & Related papers  (2024-07-27T05:55:42Z)
• HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition [92.17397504834825]
  HD-Eval is a framework that iteratively aligns large language models evaluators with human preference. HD-Eval inherits the essence from the evaluation mindset of human experts and enhances the alignment of LLM-based evaluators. Extensive experiments on three evaluation domains demonstrate the superiority of HD-Eval in further aligning state-of-the-art evaluators.
  arXiv  Detail & Related papers  (2024-02-24T08:01:32Z)
• TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness [58.721012475577716]
  Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, prompting a surge in their practical applications. This paper introduces TrustScore, a framework based on the concept of Behavioral Consistency, which evaluates whether an LLMs response aligns with its intrinsic knowledge.
  arXiv  Detail & Related papers  (2024-02-19T21:12:14Z)
• Leveraging Large Language Models for NLG Evaluation: Advances and Challenges [57.88520765782177]
  Large Language Models (LLMs) have opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. We propose a coherent taxonomy for organizing existing LLM-based evaluation metrics, offering a structured framework to understand and compare these methods. By discussing unresolved challenges, including bias, robustness, domain-specificity, and unified evaluation, this paper seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.
  arXiv  Detail & Related papers  (2024-01-13T15:59:09Z)
• Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems [0.0]
  The rise of generative artificial intelligence, particularly Large Language Models (LLMs), has intensified the imperative to scrutinize fairness alongside accuracy. Recent studies have begun to investigate fairness evaluations for LLMs within domains such as recommendations. Yet, the degree to which current fairness evaluation frameworks account for personalization remains unclear.
  arXiv  Detail & Related papers  (2024-01-08T17:57:29Z)
• Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks [65.69651759036535]
  We analyze whether large language models (LLMs) can serve as reliable alternatives to humans.<n>This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning)<n>We find that LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts.
  arXiv  Detail & Related papers  (2023-10-30T17:04:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.