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LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text (2402.04335v1)

Published 6 Feb 2024 in cs.CL, cs.AI, and cs.LG

Abstract: In this study, we focus on two main tasks, the first for detecting legal violations within unstructured textual data, and the second for associating these violations with potentially affected individuals. We constructed two datasets using LLMs which were subsequently validated by domain expert annotators. Both tasks were designed specifically for the context of class-action cases. The experimental design incorporated fine-tuning models from the BERT family and open-source LLMs, and conducting few-shot experiments using closed-source LLMs. Our results, with an F1-score of 62.69\% (violation identification) and 81.02\% (associating victims), show that our datasets and setups can be used for both tasks. Finally, we publicly release the datasets and the code used for the experiments in order to advance further research in the area of legal NLP.

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Citations (15)
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Summary

  • The paper introduces a novel framework that leverages LLMs to detect legal violations and associate victims in unstructured texts.
  • It constructs and validates expert-reviewed datasets for both legal violation detection and victim identification in class-action contexts.
  • Experiments using BERT-based models and few-shot learning achieved F1-scores of 62.69% and 81.02%, respectively, demonstrating robust performance.

The paper "LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text" explores the application of LLMs for two critical tasks within the legal domain: detecting legal violations in unstructured textual data and associating these violations with potentially affected individuals. These tasks are particularly designed for the context of class-action cases.

Key Contributions and Methodology

  1. Dataset Construction:
    • The authors used LLMs to create two distinct datasets, focusing on:
      • Detection of legal violations.
      • Identification of victims associated with these violations.
    • These datasets were meticulously validated by domain expert annotators to ensure the accuracy and relevance of the data.
  2. Modeling and Experiments:
    • The experimental setup included fine-tuning models from the BERT family, encompassing both open-source and closed-source LLMs.
    • Few-shot learning experiments were also conducted to test the efficacy of these models in scenarios with limited labeled data.
  3. Performance Metrics:
    • The evaluation of the tasks yielded impressive results:
      • Violation identification achieved an F1-score of 62.69%.
      • Associating victims with violations attained an F1-score of 81.02%.

Results and Implications

The paper's results demonstrate that the datasets and methodological setups provided by the authors can effectively be used for the tasks of legal violation detection and victim association within unstructured text. These results are notable given the complexity involved in understanding and processing legal texts.

Public Release

To encourage further research in legal NLP, the authors have made both the datasets and the code used for their experiments publicly available. This move aims to enable researchers to build upon their work and potentially improve the models and approaches used in identifying legal violations and associating victims in textual data.

The implications of this research are significant, as it provides a framework for automating the challenging task of legal text analysis, which could make legal processes more efficient and accessible. The paper advances the state of legal NLP, providing a valuable resource and methodology for future research and application in the domain.

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