The retrieval of entailing legal article sets aims to identify a concise set of legal articles that holds an entailment relationship with a legal query or its negation. Unlike traditional information retrieval that focuses on relevance ranking, this task demands conciseness. However, prior research has inadequately addressed this need by employing traditional methods. To bridge this gap, we propose a three-stage Retrieve–Revise–Refine framework which explicitly addresses the need for conciseness by utilizing both small and large language models (LMs) in distinct yet complementary roles. Empirical evaluations on the COLIEE 2022 and 2023 datasets demonstrate that our framework significantly enhances performance, achieving absolute increases in the macro F2 score by 3.17% and 4.24% over previous state-of-the-art methods, respectively. Specifically, our Retrieve stage, employing various tailored fine-tuning strategies for small LMs, achieved a recall rate exceeding 0.90 in the top-5 results alone—ensuring comprehensive coverage of entailing articles. In the subsequent Revise stage, large LMs narrow this set, improving precision while sacrificing minimal coverage. The Refine stage further enhances precision by leveraging specialized insights from small LMs, resulting in a relative improvement of up to 19.15% in the number of concise article sets retrieved compared to previous methods. Our framework offers a promising direction for further research on specialized methods for retrieving concise sets of entailing legal articles, thereby more effectively meeting the task’s demands.
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