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 article, 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 ( https://github.com/thanhtcptit/Legal-Case-Entailment-Framework ).

In this paper, we propose a pipeline leveraging Large Language Models (LLMs) for data augmentation in Information Extraction tasks within the legal domain. The proposed method is both simple and effective, significantly reducing the manual effort required for data annotation while enhancing the robustness of Information Extraction systems. Furthermore, the method is generalizable, making it applicable to various Natural Language Processing (NLP) tasks beyond the legal domain.

In recent years, there have been significant advancements in semantic parsing tasks, thanks to the introduction of pre-trained language models. However, a substantial gap persists between English and other languages due to the scarcity of annotated data. One promising strategy to bridge this gap involves augmenting multilingual datasets using labeled English data and subsequently leveraging this augmented dataset for training semantic parsers (known as zero-shot multilingual semantic parsing). In our study, we propose a novel framework to effectively perform zero-shot multilingual semantic parsing under the support of large language models (LLMs). Given data annotated pairs (sentence, semantic representation) in English, our proposed framework automatically augments data in other languages via multilingual chain-of-thought (CoT) prompting techniques that progressively construct the semantic form in these languages. By breaking down the entire semantic representation into sub-semantic fragments, our CoT prompting technique simplifies the intricate semantic structure at each step, thereby facilitating the LLMs in generating accurate outputs more efficiently. Notably, this entire augmentation process is achieved without the need for any demonstration samples in the target languages (zero-shot learning). In our experiments, we demonstrate the effectiveness of our method by evaluating it on two well-known multilingual semantic parsing datasets: MTOP and MASSIVE.