构建首个美国判例法法律论证挖掘语料库，助力法律AI研究。
LAMUS: A Large-Scale Corpus for Legal Argument Mining from U.S. Caselaw using LLMs
- 用大模型自动标注+人工精修，构建高质量语料
- 链式思考提示使大模型性能显著提升，错误率降近20%
- 适合法律NLP、AI辅助司法研究者使用
法律论证挖掘旨在识别和分类司法推理中的功能成分，如事实、问题、规则、分析和结论。该领域进展受限于缺乏大规模、高质量的美国判例法标注数据，尤其在州级层面。本文提出LAMUS，一个基于美国最高法院判决和德克萨斯州刑事上诉案件构建的句子级法律论证挖掘语料库。通过数据驱动流程，结合大规模案例收集、大模型自动标注及针对性人机协同质量优化实现。将法律论证挖掘建模为六类句子分类任务，评估通用与法律领域语言模型在零样本、少样本及链式思考提示下的表现，以LegalBERT为监督基线。结果表明，链式思考提示显著提升大模型性能，领域模型零样本表现更稳定。大模型辅助验证纠正了近20%的标注错误，提升标签一致性；人工验证达成Cohen's Kappa 0.85，证实标注质量。LAMUS为未来法律自然语言处理研究提供可扩展资源与实证洞见。所有代码与数据可在GitHub获取：https://github.com/LavanyaPobbathi/LAMUS/tree/main
原文摘要 · Abstract (English)
Legal argument mining aims to identify and classify the functional components of judicial reasoning, such as facts, issues, rules, analysis, and conclusions. Progress in this area is limited by the lack of large-scale, high-quality annotated datasets for U.S. caselaw, particularly at the state level. This paper introduces LAMUS, a sentence-level legal argument mining corpus constructed from U.S. Supreme Court decisions and Texas criminal appellate opinions. The dataset is created using a data-centric pipeline that combines large-scale case collection, LLM-based automatic annotation, and targeted human-in-the-loop quality refinement. We formulate legal argument mining as a six-class sentence classification task and evaluate multiple general-purpose and legal-domain language models under zero-shot, few-shot, and chain-of-thought prompting strategies, with LegalBERT as a supervised baseline. Results show that chain-of-thought prompting substantially improves LLM performance, while domain-specific models exhibit more stable zero-shot behavior. LLM-assisted verification corrects nearly 20% of annotation errors, improving label consistency. Human verification achieves Cohen's Kappa of 0.85, confirming annotation quality. LAMUS provides a scalable resource and empirical insights for future legal NLP research. All code and datasets can be accessed for reproducibility on GitHub at: https://github.com/LavanyaPobbathi/LAMUS/tree/main
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