RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems

• URL: http://arxiv.org/abs/2601.19637v1
• Date: Tue, 27 Jan 2026 14:13:46 GMT
• Title: RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems
• Authors: Weicong Liu, Zixuan Yang, Yibo Zhao, Xiang Li,
• Abstract summary: We introduce LR-bench, a benchmark curated from 2024-2025 AI/NLP manuscripts with five-level self-assessed familiarity ratings.<n>We also propose RATE, a reviewer-centric ranking framework that distills each reviewer's recent publications into compact keyword-based profiles.<n>Our approach consistently achieves state-of-the-art performance, outperforming strong embedding baselines by a clear margin.
• Score: 6.083097040417168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reviewer assignment is increasingly critical yet challenging in the LLM era, where rapid topic shifts render many pre-2023 benchmarks outdated and where proxy signals poorly reflect true reviewer familiarity. We address this evaluation bottleneck by introducing LR-bench, a high-fidelity, up-to-date benchmark curated from 2024-2025 AI/NLP manuscripts with five-level self-assessed familiarity ratings collected via a large-scale email survey, yielding 1055 expert-annotated paper-reviewer-score annotations. We further propose RATE, a reviewer-centric ranking framework that distills each reviewer's recent publications into compact keyword-based profiles and fine-tunes an embedding model with weak preference supervision constructed from heuristic retrieval signals, enabling matching each manuscript against a reviewer profile directly. Across LR-bench and the CMU gold-standard dataset, our approach consistently achieves state-of-the-art performance, outperforming strong embedding baselines by a clear margin. We release LR-bench at https://huggingface.co/datasets/Gnociew/LR-bench, and a GitHub repository at https://github.com/Gnociew/RATE-Reviewer-Assign.

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