用大模型做论文预审能提效，但自动评审存在严重偏差风险。
Pre-review to Peer review: Pitfalls of Automating Reviews using Large Language Models
- 用开源大模型生成审稿意见，对比真实人类评分与发表后指标。
- 模型评分与人类评分相关性仅0.15，普遍高估3-5分且自信度超8分。
- 模型审稿意见比人类更贴近论文后续引用、创新性等发表表现。
大语言模型在学术同行评审中可作为预审助手，但完全自动化评审存在安全与研究完整性风险。本文基于OpenReview的真实评审数据，使用多个前沿开源大模型生成审稿意见，评估其在科学评审流程中的可靠性。结果显示，所有模型与人类评审者相关性仅为0.15，存在3-5分系统性高估，且预测错误时仍保持8.0-9.0/10的高置信度。然而，模型评分与论文后续的引用量（Citations）、热点论文（Hit-papers）、新颖性（Novelty）及颠覆性（Disruption）等指标相关性更强，表明其在预审阶段仍有价值。研究开源了数据集$D_{LMRSD}$，以推动科学评审自动化安全框架建设。
原文摘要 · Abstract (English)
Large Language Models are versatile general-task solvers, and their capabilities can truly assist people with scholarly peer review as \textit{pre-review} agents, if not as fully autonomous \textit{peer-review} agents. While incredibly beneficial, automating academic peer-review, as a concept, raises concerns surrounding safety, research integrity, and the validity of the academic peer-review process. The majority of the studies performing a systematic evaluation of frontier LLMs generating reviews across science disciplines miss the mark on addressing the alignment/misalignment of reviews along with the utility of LLM generated reviews when compared against publication outcomes such as \textbf{Citations}, \textbf{Hit-papers}, \textbf{Novelty}, and \textbf{Disruption}. This paper presents an experimental study in which we gathered ground-truth reviewer ratings from OpenReview and used various frontier open-weight LLMs to generate reviews of papers to gauge the safety and reliability of incorporating LLMs into the scientific review pipeline. Our findings demonstrate the utility of frontier open-weight LLMs as pre-review screening agents despite highlighting fundamental misalignment risks when deployed as autonomous reviewers. Our results show that all models exhibit weak correlation with human peer reviewers (0.15), with systematic overestimation bias of 3-5 points and uniformly high confidence scores (8.0-9.0/10) despite prediction errors. However, we also observed that LLM reviews correlate more strongly with post-publication metrics than with human scores, suggesting potential utility as pre-review screening tools. Our findings highlight the potential and address the pitfalls of automating peer reviews with language models. We open-sourced our dataset $D_{LMRSD}$ to help the research community expand the safety framework of automating scientific reviews.
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