Retrieving superior legal articles involves identifying relevant legal articles that hold higher legal effectiveness. This process is crucial in legislative work because superior legal articles form the legal basis for drafting new laws. However, most existing legal information retrieval research focuses on retrieving legal documents, with limited research on retrieving superior legal articles. This gap restricts the digitization of legislative work. To advance research in this area, we propose SLARD: A Chinese Superior Legal Article Retrieval Dataset, which filters 2,627 queries and 9,184 candidates from over 4.3 million effective Chinese regulations, covering 32 categories, such as environment, agriculture, and water resources. Each query is manually annotated, and the candidates include superior articles at both the provincial and national levels. We conducted detailed experiments and analyses on the dataset and found that existing retrieval methods struggle to achieve ideal results. The best method achieved a R@1 of only 0.4719. Additionally, we found that existing large language models (LLMs) lack prior knowledge of the content of superior legal articles. This indicates the necessity for further exploration and research in this field.

Legal Judgement Prediction (LJP) is the task of automatically predicting a law case’s judgment results given a text describing the case’s facts, which has great prospects in judicial assistance systems and handy services for the public. In practice, confusing charges are often presented, because law cases applicable to similar law articles are easily misjudged. To address this issue, existing work relies heavily on domain experts, which hinders its application in different law systems. In this paper, we present an end-to-end model, LADAN, to solve the task of LJP. To distinguish confusing charges, we propose a novel graph neural network, GDL, to automatically learn subtle differences between confusing law articles, and also design a novel attention mechanism that fully exploits the learned differences to attentively extract effective discriminative features from fact descriptions. Experiments conducted on real-world datasets demonstrate the superiority of our LADAN.