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Legal case retrieval plays an important role for legal practitioners toeffectively retrieve relevant cases given a query case. Most existing neurallegal case retrieval models directly encode the whole legal text of a case togenerate a case representation, which is then utilised to conduct a nearestneighbour search for retrieval. Although these straightforward methods haveachieved improvement over conventional statistical methods in retrievalaccuracy, two significant challenges are identified in this paper: (1) Legalfeature alignment: the usage of the whole case text as the input will generallyincorporate redundant and noisy information because, from the legalperspective, the determining factor of relevant cases is the alignment of keylegal features instead of whole text matching; (2) Legal context preservation:furthermore, since the existing text encoding models usually have an inputlength limit shorter than the case, the whole case text needs to be truncatedor divided into paragraphs, which leads to the loss of the global context oflegal information. In this paper, a novel legal case retrieval framework,PromptCase, is proposed to tackle these challenges. Firstly, legal facts andlegal issues are identified and formally defined as the key featuresfacilitating legal case retrieval based on a thorough study of the definitionof relevant cases from a legal perspective. Secondly, with the determininglegal features, a prompt-based encoding scheme is designed to conduct aneffective encoding with language models. Extensive zero-shot experiments havebeen conducted on two benchmark datasets in legal case retrieval, whichdemonstrate the superior retrieval effectiveness of the proposed PromptCase.The code has been released on https://github.com/yanran-tang/PromptCase.

Prompt-based Effective Input Reformulation for Legal Case Retrieval