← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.600 · 记录 #1
GLARE: Agentic Reasoning for Legal Judgment Prediction
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:00
摘要
Legal judgment prediction serves as a pivotal task in intelligent judicial systems. Although large language models have achieved remarkable progress in general reasoning, they struggle with tasks that require fine-grained distinctions between similar charges. These models often select plausible charges directly without discriminating among closely related alternatives. In this paper, we introduce GLARE, an agentic legal reasoning framework that enables models to actively retrieve and apply external knowledge during decision-making. Unlike static prediction, GLARE simulates comparative reasoning by dynamically expanding the decision space to include confusing candidates, then retrieving exclusionary logic from precedents and statutes to identify the correct judgment. Experiments on real-world datasets show that our method significantly outperforms strong baselines, especially on complex cases involving confusing or rare charges. The code is available at https://anonymous.4open.science/r/GLARE-LJP-8EDF.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
GLARE: Agentic Reasoning for Legal Judgment Prediction
Legal judgment prediction serves as a pivotal task in intelligent judicial systems. Although large language models have achieved remarkable progress in general reasoning, they struggle with tasks that require fine-grained distinctions between similar charges. These models often select plausible charges directly without discriminating among closely related alternatives. In this paper, we introduce GLARE, an agentic legal reasoning framework that enables models to actively retrieve and apply external knowledge during decision-making. Unlike static prediction, GLARE simulates comparative reasoning by dynamically expanding the decision space to include confusing candidates, then retrieving exclusionary logic from precedents and statutes to identify the correct judgment. Experiments on real-world datasets show that our method significantly outperforms strong baselines, especially on complex cases involving confusing or rare charges. The code is available at https://anonymous.4open.science/r/GLARE-LJP-8EDF.
查看此版本解读 →查看此版本原文 ↗