CADLRA: A multi-charge prediction method based on the Criminal Act-Driven Law Retrieval Augmentation

Jianzhou Feng, Lazhi Zhao, Haonan Qin, Yiming Xu, Ziqi Wang

Abstract: Legal Artificial Intelligence (Legal AI) has garnered significant attention in both academic and industrial domains in recent years. However, most legal judgment prediction (LJP) methods concentrate on single-charge prediction tasks, ignoring the practical scenario of “one person with multiple charges”. To mitigate this limitation, we propose a multi-charge prediction method based on the Criminal Act-Driven Law Retrieval Augmentation (CADLRA), which utilizes Large Language Models (LLMs) to convert the multi-charge classification task into a dynamic multi-charge generation task, achieving enhanced prediction accuracy. To address knowledge solidification and hallucination in LLMs and align with the legal process of sentencing based on criminal acts and relevant laws, we employ contrastive learning to train a retriever to aid LLMs in charge prediction by referencing prior law articles. Finally, experiments were conducted using the public dataset from the Legal AI Challenge, demonstrating that the CADLRA method has achieved state-of-the-art results in both multi-label classification algorithms and charge prediction.