用大模型分析审稿意见，自动发现并改进问题，提升审稿质量。
Reviewing the Reviewer: Elevating Peer Review Quality through LLM-Guided Feedback
- 拆解审稿内容为论证片段，结合大模型与传统分类器识别多类问题
- 生成针对性改进建议，实验显示审稿质量最高提升92.4%
- 适用于希望提高审稿水平的研究者和期刊编辑
同行评审是科学质量的核心，但依赖简单启发式方法——懒惰思维——已导致标准下降。以往工作将懒惰思维检测视为单标签任务，但审稿段落可能同时存在清晰度、具体性等多重问题。将检测转化为可操作的改进需具备指南意识的反馈，目前尚缺。我们提出一种基于大模型的框架：将审稿意见分解为论证片段，通过神经符号模块结合大模型特征与传统分类器识别问题，并利用遗传算法优化的问题专属模板生成精准反馈。实验表明，该方法优于零样本大模型基线，审稿质量最高提升92.4%。我们还发布了LazyReviewPlus数据集，包含1,309条标注了懒惰思维与具体性的句子。
原文摘要 · Abstract (English)
Peer review is central to scientific quality, yet reliance on simple heuristics -- lazy thinking -- has lowered standards. Prior work treats lazy thinking detection as a single-label task, but review segments may exhibit multiple issues, including broader clarity problems, or specificity issues. Turning detection into actionable improvements requires guideline-aware feedback, which is currently missing. We introduce an LLM-driven framework that decomposes reviews into argumentative segments, identifies issues via a neurosymbolic module combining LLM features with traditional classifiers, and generates targeted feedback using issue-specific templates refined by a genetic algorithm. Experiments show our method outperforms zero-shot LLM baselines and improves review quality by up to 92.4\%. We also release LazyReviewPlus, a dataset of 1,309 sentences labeled for lazy thinking and specificity.
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