Topic Modelling Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment

• URL: http://arxiv.org/abs/2405.12910v1
• Date: Tue, 21 May 2024 16:30:25 GMT
• Title: Topic Modelling Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment
• Authors: Holli Sargeant, Ahmed Izzidien, Felix Steffek,
• Abstract summary: This paper develops and applies 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Legal Evalutions and Challenges of Large Language Models [42.51294752406578]
  We use the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain.
  arXiv  Detail & Related papers  (2024-11-15T12:23:12Z)
• DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
  We introduce DELTA, a discriminative model designed for legal case retrieval. We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability. Our approach can outperform existing state-of-the-art methods in legal case retrieval.
  arXiv  Detail & Related papers  (2024-03-27T10:40:14Z)
• LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset [0.0]
  This study addresses the gap in the literature working with large legal corpora about how to isolate cases, in our case summary judgments, from a large corpus of UK court decisions. We use the Cambridge Law Corpus of 356,011 UK court decisions and determine that the large language model achieves a weighted F1 score of 0.94 versus 0.78 for keywords. We identify and extract 3,102 summary judgment cases, enabling us to map their distribution across various UK courts over a temporal span.
  arXiv  Detail & Related papers  (2024-03-04T10:13:30Z)
• Discovering Significant Topics from Legal Decisions with Selective Inference [0.0]
  We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts. The method identifies case topics significantly correlated with outcomes, topic-word distributions and case-topic weights. We show that topics derived by the pipeline are consistent with legal doctrines in both areas and can be useful in other related legal analysis tasks.
  arXiv  Detail & Related papers  (2024-01-02T07:00:24Z)
• Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration [52.57055162778548]
  Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
  arXiv  Detail & Related papers  (2023-10-13T16:47:20Z)
• Enhancing Pre-Trained Language Models with Sentence Position Embeddings for Rhetorical Roles Recognition in Legal Opinions [0.16385815610837165]
  The size of legal opinions continues to grow, making it increasingly challenging to develop a model that can accurately predict the rhetorical roles of legal opinions. We propose a novel model architecture for automatically predicting rhetorical roles using pre-trained language models (PLMs) enhanced with knowledge of sentence position information. Based on an annotated corpus from the LegalEval@SemEval2023 competition, we demonstrate that our approach requires fewer parameters, resulting in lower computational costs.
  arXiv  Detail & Related papers  (2023-10-08T20:33:55Z)
• An Intent Taxonomy of Legal Case Retrieval [43.22489520922202]
  Legal case retrieval is a special Information Retrieval(IR) task focusing on legal case documents. We present a novel hierarchical intent taxonomy of legal case retrieval. We reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval.
  arXiv  Detail & Related papers  (2023-07-25T07:27:32Z)
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval [75.05173891207214]
  Legal case retrieval plays a core role in the intelligent legal system. Most existing language models have difficulty understanding the long-distance dependencies between different structures. We propose a new Structure-Aware pre-traIned language model for LEgal case Retrieval.
  arXiv  Detail & Related papers  (2023-04-22T10:47:01Z)
• Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction [46.71918729837462]
  Given the fact description text of a legal case, legal judgment prediction aims to predict the case's charge, law article and penalty term. Previous studies fail to distinguish different classification errors with a standard cross-entropy classification loss. We propose a moco-based supervised contrastive learning to learn distinguishable representations. We further enhance the representation of the fact description with extracted crime amounts which are encoded by a pre-trained numeracy model.
  arXiv  Detail & Related papers  (2022-11-15T15:53:56Z)
• Entity Graph Extraction from Legal Acts -- a Prototype for a Use Case in Policy Design Analysis [52.77024349608834]
  This paper presents a prototype developed to serve the quantitative study of public policy design. Our system aims to automate the process of gathering legal documents, annotating them with Institutional Grammar, and using hypergraphs to analyse inter-relations between crucial entities.
  arXiv  Detail & Related papers  (2022-09-02T10:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.