LePaRD: A Large-Scale Dataset of Judges Citing Precedents

• URL: http://arxiv.org/abs/2311.09356v3
• Date: Tue, 01 Oct 2024 14:09:08 GMT
• Title: LePaRD: A Large-Scale Dataset of Judges Citing Precedents
• Authors: Robert Mahari, Dominik Stammbach, Elliott Ash, Alex `Sandy' Pentland,
• Abstract summary: LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. Legal passage prediction seeks to predict relevant passages from precedential court decisions. A subset of the LePaRD dataset is freely available and the whole dataset will be released upon publication.
• Score: 11.163288406795335
• License:
• Abstract: We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate work on legal passage prediction, a challenging practice-oriented legal retrieval and reasoning task. Legal passage prediction seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various retrieval approaches on LePaRD, and find that classification appears to work best. However, we note that legal precedent prediction is a difficult task, and there remains significant room for improvement. We hope that by publishing LePaRD, we will encourage others to engage with a legal NLP task that promises to help expand access to justice by reducing the burden associated with legal research. A subset of the LePaRD dataset is freely available and the whole dataset will be released upon publication.

Related papers

• Query-driven Relevant Paragraph Extraction from Legal Judgments [1.2562034805037443]
  Legal professionals often grapple with navigating lengthy legal judgements to pinpoint information that directly address their queries. This paper focus on this task of extracting relevant paragraphs from legal judgements based on the query. We construct a specialized dataset for this task from the European Court of Human Rights (ECtHR) using the case law guides.
  arXiv  Detail & Related papers  (2024-03-31T08:03:39Z)
• 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)
• 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)
• Prototype-Based Interpretability for Legal Citation Prediction [16.660004925391842]
  We design the task with parallels to the thought-process of lawyers, i.e., with reference to both precedents and legislative provisions. After initial experimental results, we refine the target citation predictions with the feedback of legal experts. We introduce a prototype architecture to add interpretability, achieving strong performance while adhering to decision parameters used by lawyers.
  arXiv  Detail & Related papers  (2023-05-25T21:40:58Z)
• 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)
• Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset [46.156169284961045]
  We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material. First, we gather and make available the Pile of Law, a 256GB dataset of open-source English-language legal and administrative data. Second, we distill the legal norms that governments have developed to constrain the inclusion of toxic or private content into actionable lessons. Third, we show how the Pile of Law offers researchers the opportunity to learn such filtering rules directly from the data.
  arXiv  Detail & Related papers  (2022-07-01T06:25:15Z)
• JUSTICE: A Benchmark Dataset for Supreme Court's Judgment Prediction [0.0]
  We aim to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications. By using advanced NLP algorithms to analyze previous court cases, the trained models are able to predict and classify a court's judgment.
  arXiv  Detail & Related papers  (2021-12-06T23:19:08Z)
• Legal Judgment Prediction with Multi-Stage CaseRepresentation Learning in the Real Court Setting [25.53133777558123]
  We introduce a novel dataset from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction.
  arXiv  Detail & Related papers  (2021-07-12T04:27:14Z)
• AutoLAW: Augmented Legal Reasoning through Legal Precedent Prediction [0.0]
  This paper demonstrate how NLP can be used to address an unmet need of the legal community and increase access to justice. The paper introduces Legal Precedent Prediction (LPP), the task of predicting relevant passages from precedential court decisions given the context of a legal argument.
  arXiv  Detail & Related papers  (2021-06-30T13:01:33Z)
• 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)

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.