Legal Summarisation through LLMs: The PRODIGIT Project

• URL: http://arxiv.org/abs/2308.04416v1
• Date: Fri, 4 Aug 2023 16:59:48 GMT
• Title: Legal Summarisation through LLMs: The PRODIGIT Project
• Authors: Thiago Dal Pont and Federico Galli and Andrea Loreggia and Giuseppe Pisano and Riccardo Rovatti and Giovanni Sartor
• Abstract summary: PRODIGIT aims to support tax judges and lawyers through digital technology, focusing on AI. We have focused on generation of summaries of judicial decisions and on the extraction of related information. We have deployed and evaluated different tools and approaches to extractive and abstractive summarisation.
• Score: 4.840725842638346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present some initial results of a large-scale Italian project called PRODIGIT which aims to support tax judges and lawyers through digital technology, focusing on AI. We have focused on generation of summaries of judicial decisions and on the extraction of related information, such as the identification of legal issues and decision-making criteria, and the specification of keywords. To this end, we have deployed and evaluated different tools and approaches to extractive and abstractive summarisation. We have applied LLMs, and particularly on GPT4, which has enabled us to obtain results that proved satisfactory, according to an evaluation by expert tax judges and lawyers. On this basis, a prototype application is being built which will be made publicly available.

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)
• 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)
• Bridging Law and Data: Augmenting Reasoning via a Semi-Structured Dataset with IRAC methodology [22.740895683854568]
  This paper introduces LEGALSEMI, a benchmark specifically curated for legal scenario analysis. LEGALSEMI comprises 54 legal scenarios, each rigorously annotated by legal experts, based on the comprehensive IRAC (Issue, Rule, Application, Conclusion) framework. A series of experiments were conducted to assess the usefulness of LEGALSEMI for IRAC analysis.
  arXiv  Detail & Related papers  (2024-06-19T04:59:09Z)
• Automatic Information Extraction From Employment Tribunal Judgements Using Large Language Models [0.4810407297181484]
  This paper presents a study on the application of GPT-4, a large language model, for automatic information extraction from UK employment tribunal cases. We meticulously evaluated GPT-4's performance in extracting critical information with a manual verification process.
  arXiv  Detail & Related papers  (2024-03-19T17:43:08Z)
• Large Language Models in Law: A Survey [34.785207813971134]
  The application of legal large language models (LLMs) is still in its nascent stage. We provide an overview of AI technologies in the legal field and showcase the recent research in LLMs. We explore the limitations of legal LLMs, including data, algorithms, and judicial practice.
  arXiv  Detail & Related papers  (2023-11-26T00:48:12Z)
• Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies [0.7673339435080445]
  We propose a novel framework facilitating effective collaboration of a legal expert with a large language model (LLM) We employed the framework for an analysis of a dataset (n=785) of facts descriptions from criminal court opinions regarding thefts.
  arXiv  Detail & Related papers  (2023-10-28T15:20:44Z)
• A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction [60.70089334782383]
  Large language models (LLMs) have demonstrated great potential for domain-specific applications. Recent disputes over GPT-4's law evaluation raise questions concerning their performance in real-world legal tasks. We design practical baseline solutions based on LLMs and test on the task of legal judgment prediction.
  arXiv  Detail & Related papers  (2023-10-18T07:38:04Z)
• 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)
• An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
  We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago. We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
  arXiv  Detail & Related papers  (2022-03-08T15:46:52Z)
• 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.