LAMUS: A Large-Scale Corpus for Legal Argument Mining from U.S. Caselaw using LLMs
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 senten...
| Authors | Serene Wang, Lavanya Pobbathi, Haihua Chen |
|---|---|
| Primary category | cs.CL |
| Published | 9 March 2026 |
| Last updated at source | 29 July 2026 |
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