让人类先下判断，AI扩展成评论，提升审稿自动化与责任性平衡。
Judgment-Grounded Expansion for Peer Review Generation
- 人类给出评价结论，AI生成对应评论候选
- 用置信区间控制候选集大小，确保覆盖目标评论
- 适合需要可解释审稿的学术协作场景
自动审稿有助于加速科学发展。现有方法多采用端到端模式，但完全自动化难以满足问责需求。本文提出判断基扩展（judgment-grounded expansion）的人机协作范式：评审人提供评估主张，系统将其扩展为评论候选。我们将其建模为结构化生成-检查-优化流程，并通过用户研究收集了人机交互数据。针对可扩展评估和候选集筛选两个实际挑战，我们开发了大规模评估模拟方法，发现分位数预测（conformal prediction）能有效平衡候选数量与覆盖率。本工作确立了该任务的具体形式，并为未来协同审稿系统的设计提供了实证与方法基础。
原文摘要 · Abstract (English)
Automatic review generation is a promising direction for accelerating scientific progress. While most work adopts an end-to-end setup, its fully automated nature makes it less suitable for settings that demand accountability. To better balance automation and accountability, we formalize judgment-grounded expansion, a human-AI collaboration mode where a reviewer provides an evaluative claim and the system expands it into review comment candidate(s). We model it as a structured generate-check-refine process and conduct a user study to collect human-model interaction data. We study two practical challenges for judgment-grounded expansion: scalable evaluation and candidate set curation. We develop methods to simulate the process for large-scale evaluation, and show that conformal prediction is well suited to balancing candidate set size and target coverage. Our work establishes judgment-grounded expansion as a concrete task and provides empirical and methodological foundations for the design of future collaborative review generation systems.
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