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How to Use Large Language Models for Empirical Legal Research

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  • Jonathan H. Choi

Abstract

Legal scholars have long annotated cases by hand to summarize and learn about developments in jurisprudence. Dramatic recent improvements in the performance of large language models (LLMs) now provide a potential alternative. This article demonstrates how to use LLMs to analyze legal documents. It evaluates best practices and suggests both the uses and potential limitations of LLMs in empirical legal research. In a simple classification task involving Supreme Court opinions, it finds that GPT-4 performs approximately as well as human coders and significantly better than a variety of prior-generation natural language processing (NLP) classifiers, with no improvement from supervised training, finetuning, or specialized prompting.

Suggested Citation

  • Jonathan H. Choi, 2024. "How to Use Large Language Models for Empirical Legal Research," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 180(2), pages 214-233.
    • Handle: RePEc:mhr:jinste:urn:doi:10.1628/jite-2024-0006
    DOI: 10.1628/jite-2024-0006
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    Keywords

    large language models; LLMs; artificial intelligence; AI; empirical legal studies; computational analysis of law; natural language processing; machine learning JEL classification;
    All these keywords.

    JEL classification:

    • K00 - Law and Economics - - General - - - General (including Data Sources and Description)

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