  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.
