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New benchmark and LLM framework enhance academic peer review recommendations

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced OmniReview, a large-scale benchmark dataset and an LLM-enhanced framework designed to improve realistic reviewer recommendations in academic peer review. The dataset comprises over 200,000 verified review records, sourced from multiple academic platforms and processed through a disambiguation pipeline. To address limitations in existing embedding-based approaches, the proposed framework, Pro-MMoE, integrates Large Language Models with Multi-task Learning to capture nuanced expertise and balance conflicting evaluation goals, achieving state-of-the-art performance on several metrics. AI

IMPACT This work could lead to more efficient and accurate academic peer review processes through improved reviewer recommendations.

RANK_REASON The cluster describes a new academic paper introducing a benchmark dataset and a novel framework for a specific research problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark and LLM framework enhance academic peer review recommendations

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The cluster describes a new academic paper introducing a benchmark dataset and a novel framework for a specific research problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yehua Huang, Penglei Sun, Zebin Chen, Zhenheng Tang, Xiaowen Chu ·

    OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation

    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…

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