LAMUS: A Large-Scale Corpus for Legal Argument Mining from U.S. Caselaw Using LLMs

Abstract

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, large language model (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 𝜅 = 0.85, confirming annotation quality. LAMUS provides a scalable resource and empirical insights for future legal natural language processing research. All code and datasets can be accessed for reproducibility in GitHub https://github.com/LavanyaPobbathi/LAMUS.

Received: 15 March 2026 | Revised: 4 June 2026 | Accepted: 30 June 2026

Conflicts of Interest

Haihua Chen is the Editorial Board Member for the Journal of Computational Law and Legal Technology and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in LAMUS at https://github.com/LavanyaPobbathi/LAMUS/tree/main.

Author Contribution Statement

Serene Wang: Methodology, Validation, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Lavanya Pobbathi: Software, Validation, Formal analysis, Investigation, Data curation, Writing – review & editing, Visualization. Haihua Chen: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.