LePaRD: A Large-Scale Dataset of Judges Citing Precedents

Robert Mahari, Dominik Stammbach, Elliott Ash, Alex `Sandy' Pentland
11/15/2023
cs.CL

No official code found. Best implementation: rmahari/LePaRD (11 stars, last commit 2025-01-31).

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.

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Code Implementations(1)

rmahari/LePaRDCommunity100%
111PythonNov 14, 20231 years agoNOASSERTION

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Cite this paper

@article{mahari2023lepard,
  title  = {LePaRD: A Large-Scale Dataset of Judges Citing Precedents},
  author = {Robert Mahari and Dominik Stammbach and Elliott Ash and Alex `Sandy' Pentland},
  year   = {2023},
  eprint = {2311.09356},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.09356v3}
}

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