Topic Modelling Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment
<|reference_start|>Topic Modelling Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment: This paper addresses a critical gap in legal analytics by developing and applying a novel taxonomy for topic modelling summary judgment cases in the United Kingdom. Using a curated dataset of summary judgment cases, we use the Large Language Model Claude 3 Opus to explore functional topics and trends. We find that Claude 3 Opus correctly classified the topic with an accuracy of 87.10%. The analysis reveals distinct patterns in the application of summary judgments across various legal domains. As case law in the United Kingdom is not originally labelled with keywords or a topic filtering option, the findings not only refine our understanding of the thematic underpinnings of summary judgments but also illustrate the potential of combining traditional and AI-driven approaches in legal classification. Therefore, this paper provides a new and general taxonomy for UK law. The implications of this work serve as a foundation for further research and policy discussions in the field of judicial administration and computational legal research methodologies.<|reference_end|>
@article{sargeant2024topic,
    title={Topic Modelling Case Law Using a Large Language Model and a New Taxonomy
  for UK Law: AI Insights into Summary Judgment},
    author={Holli Sargeant, Ahmed Izzidien, Felix Steffek},
    journal={arXiv preprint arXiv:2405.12910},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12910},
    primaryClass={cs.CL cs.AI cs.CY cs.LG}
}