Automated Argument Generation from Legal Facts

• URL: http://arxiv.org/abs/2310.05680v3
• Date: Thu, 12 Oct 2023 04:47:45 GMT
• Title: Automated Argument Generation from Legal Facts
• Authors: Oscar Tuvey, Procheta Sen
• Abstract summary: The number of cases submitted to the law system is far greater than the available number of legal professionals in a country. In this study we partcularly focus on helping legal professionals in the process of analyzing a legal case. Experimental results show that the generated arguments from the best performing method have on average 63% overlap with the benchmark set gold standard annotations.
• Score: 6.057773749499076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The count of pending cases has shown an exponential rise across nations (e.g., with more than 10 million pending cases in India alone). The main issue lies in the fact that the number of cases submitted to the law system is far greater than the available number of legal professionals present in a country. Given this worldwide context, the utilization of AI technology has gained paramount importance to enhance the efficiency and speed of legal procedures. In this study we partcularly focus on helping legal professionals in the process of analyzing a legal case. Our specific investigation delves into harnessing the generative capabilities of open-sourced large language models to create arguments derived from the facts present in legal cases. Experimental results show that the generated arguments from the best performing method have on average 63% overlap with the benchmark set gold standard annotations.

Related papers

• LawLLM: Law Large Language Model for the US Legal System [43.13850456765944]
  We introduce the Law Large Language Model (LawLLM), a multi-task model specifically designed for the US legal domain. LawLLM excels at Similar Case Retrieval (SCR), Precedent Case Recommendation (PCR), and Legal Judgment Prediction (LJP) We propose customized data preprocessing techniques for each task that transform raw legal data into a trainable format.
  arXiv  Detail & Related papers  (2024-07-27T21:51:30Z)
• Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation [22.85652668826498]
  This paper introduces KELLER, a legal knowledge-guided case reformulation approach based on large language models (LLMs) By incorporating professional legal knowledge about crimes and law articles, we enable large language models to accurately reformulate the original legal case into concise sub-facts of crimes.
  arXiv  Detail & Related papers  (2024-06-28T08:59:45Z)
• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• 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)
• Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval [18.058942674792604]
  We propose a novel few-shot workflow tailored to the relevant judgment of legal cases. By comparing the relevance judgments of LLMs and human experts, we empirically show that we can obtain reliable relevance judgments.
  arXiv  Detail & Related papers  (2024-03-27T09:46:56Z)
• 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)
• 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)
• Mining Legal Arguments in Court Decisions [43.09204050756282]
  We develop a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights. Second, we compile and annotate a large corpus of 373 court decisions. Third, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain.
  arXiv  Detail & Related papers  (2022-08-12T08:59:55Z)
• Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents [56.40163943394202]
  We release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding. We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering.
  arXiv  Detail & Related papers  (2021-05-09T09:39:25Z)
• How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence [81.04070052740596]
  Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. This paper introduces the history, the current state, and the future directions of research in LegalAI.
  arXiv  Detail & Related papers  (2020-04-25T14:45:15Z)

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.