# Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment arXiv: https://arxiv.org/abs/2405.12910 ยท HTML: https://mlsanity.com/paper/2405.12910/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 91 references that are in the map. - First posted on arXiv 21 May 2024. ## Builds on (most cited first, 28 of 28) - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023) - [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021) - [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021) - [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022) - [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023) - [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024) - [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022) - [Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning](https://mlsanity.com/paper/2402.13950/) (2024) - [Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models](https://mlsanity.com/paper/2402.04614/) (2024) - [Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs](https://mlsanity.com/paper/2310.11689/) (2023) - [Efficient Prompting Methods for Large Language Models: A Survey](https://mlsanity.com/paper/2404.01077/) (2024) - [Think Twice Before Trusting: Self-Detection for Large Language Models through Comprehensive Answer Reflection](https://mlsanity.com/paper/2403.09972/) (2024) - [Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra](https://mlsanity.com/paper/2404.03647/) (2024) - [Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling](https://mlsanity.com/paper/2403.16248/) (2024) - [Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies](https://mlsanity.com/paper/2310.18729/) (2023) - [Prompting Large Language Models for Topic Modeling](https://mlsanity.com/paper/2312.09693/) (2023) - [SELF: Self-Evolution with Language Feedback](https://mlsanity.com/paper/2310.00533/) (2023) - [DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM](https://mlsanity.com/paper/2310.15296/) (2023) - [The Cambridge Law Corpus: A Dataset for Legal AI Research](https://mlsanity.com/paper/2309.12269/) (2023) - [Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments](https://mlsanity.com/paper/1904.06470/) (2019) - [When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models](https://mlsanity.com/paper/2404.09129/) (2024) - [LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset](https://mlsanity.com/paper/2403.04791/) (2024) - [Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling](https://mlsanity.com/paper/2405.00611/) (2024) - [Refining the Responses of LLMs by Themselves](https://mlsanity.com/paper/2305.04039/) (2023) - [Combining topic modelling and citation network analysis to study case law from the European Court on Human Rights on the right to respect for private and family life](https://mlsanity.com/paper/2401.16429/) (2024) - [A Contextual Topic Modeling and Content Analysis of Iranian laws and Regulations](https://mlsanity.com/paper/2309.13051/) (2023) - [Discovering Significant Topics from Legal Decisions with Selective Inference](https://mlsanity.com/paper/2401.01068/) (2024) ## Built on by (most cited first, 3 of 3) - [Method for Aggregating Unstructured Data Using Large Language Models](https://mlsanity.com/paper/2604.16425/) (2026) - [Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts](https://mlsanity.com/paper/2604.17674/) (2026) - [DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods](https://mlsanity.com/paper/2607.15879/) (2026) ## Abstract (arXiv) This paper addresses a critical gap in legal analytics by developing and applying a novel taxonomy for topic classification of 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 87.13% and an F1 score of 0.87. 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.