English(EN) OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation

新的基准和LLM框架增强学术同行评审推荐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员推出了OmniReview，这是一个大规模基准数据集和一个增强LLM的框架，旨在改进学术同行评审中真实的审稿人推荐。该数据集包含超过20万条经过验证的评审记录，来源于多个学术平台，并通过消歧管道进行处理。为了解决现有基于嵌入的方法的局限性，提出的Pro-MMoE框架将大型语言模型与多任务学习相结合，以捕捉细微的专业知识并平衡冲突的评估目标，在多项指标上取得了最先进的性能。 AI

影响这项工作可能通过改进审稿人推荐，从而实现更高效和准确的学术同行评审过程。

排序理由该集群描述了一篇介绍基准数据集和针对特定研究问题的新颖框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 35 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一篇介绍基准数据集和针对特定研究问题的新颖框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yehua Huang, Penglei Sun, Zebin Chen, Zhenheng Tang, Xiaowen Chu · 2026-11-11 05:00

OmniReview：一个用于真实审稿人推荐的大规模基准和LLM增强框架

arXiv:2602.08896v2 Announce Type: replace-cross Abstract: Academic peer review remains the cornerstone of scholarly validation, yet the field faces some challenges in data and methods. From the data perspective, existing research is hindered by the scarcity of large-scale, verifi…

本文如何被排名

报道来源 [1]

OmniReview：一个用于真实审稿人推荐的大规模基准和LLM增强框架

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