Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies
This work addresses the challenge of integrating LLMs into qualitative analysis for legal researchers, though it is incremental as it builds on existing LLM capabilities for a specific domain.
The authors tackled the problem of automating thematic analysis in empirical legal studies by proposing a collaborative framework between a legal expert and GPT-4, which generated reasonable initial codes, improved them with feedback, and performed well in zero-shot classification on a dataset of 785 theft descriptions.
Thematic analysis and other variants of inductive coding are widely used qualitative analytic methods within empirical legal studies (ELS). We propose a novel framework facilitating effective collaboration of a legal expert with a large language model (LLM) for generating initial codes (phase 2 of thematic analysis), searching for themes (phase 3), and classifying the data in terms of the themes (to kick-start phase 4). We employed the framework for an analysis of a dataset (n=785) of facts descriptions from criminal court opinions regarding thefts. The goal of the analysis was to discover classes of typical thefts. Our results show that the LLM, namely OpenAI's GPT-4, generated reasonable initial codes, and it was capable of improving the quality of the codes based on expert feedback. They also suggest that the model performed well in zero-shot classification of facts descriptions in terms of the themes. Finally, the themes autonomously discovered by the LLM appear to map fairly well to the themes arrived at by legal experts. These findings can be leveraged by legal researchers to guide their decisions in integrating LLMs into their thematic analyses, as well as other inductive coding projects.