Legal Case Entailment via Optimal Transport–Enhanced Retrieval and Large Language Model Ranking

Legal case entailment embodies a fundamental principle of the legal system, wherein the verdict of historical cases functions as a guiding precedent for subsequent cases sharing analogous factual circumstances. Due to the intricate nature of legal case documents, identifying entailment between legal cases requires considerable time and effort, necessitating a thorough understanding and specialized expertise in legal interpretation and analysis. To accelerate the process of legal case entailment, in this paper, we conceptualize this task as a document retrieval problem and propose a two-stage framework focused on entailment information retrieval. Within this framework, we develop a cost-efficient system that utilizes advanced language models for legal case entailment. In the first stage, we present the established ColBERT document retrieval model, augmented with a sparse keyword alignment strategy utilizing the Unbalanced Optimal Transport framework. Our study illustrates that by focusing on the interaction of contextually and semantically similar keyword pairs between the query and the document, the proposed alignment method improves the retrieval capability of ColBERT in the legal domain. For the second stage, we employ a fine-tuned MonoT5 document ranking model to refine the retrieval results and predict entailment instances. Extensive evaluation demonstrates a significant performance improvement of the proposed system compared to previous methods. As an additional study, we benchmark state-of-the-art open-source LLMs in legal case entailment to reveal their performance and potential applications. Our findings indicate that while LLMs exhibit sensitivity to prompt formulation, they demonstrate promising zero-shot performance in legal entailment scenarios. To encourage further AI development in the legal domain, we provide the code necessary to reproduce our results.