构建中文法律RAG专用基准，提升法律条款生成准确性
Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents
- 设计法律条款边界分割索引与双路径自反思机制
- 在480份法律文档上实现准确率提升1.3%至5.6%
- 适合法律AI研发与司法智能化系统开发者
检索增强生成（RAG）在中文法律场景中面临两大挑战：缺乏支持检索器-生成器联合评估的专用基准，且主流系统难以处理法律条文的结构化特性。为此，本研究提出两个核心贡献：首先，构建Legal-DC基准数据集，包含480份法律文档（涵盖市场监管、合同管理等领域）和2,475个经条款级标注的问答对；其次，提出LegRAG框架，通过法律自适应索引（条款边界分割）与双路径自反思机制，保障条款完整性并提升答案准确率。此外，引入面向大模型的自动化评估方法，满足法律检索高可靠性需求。实验表明，LegRAG在关键指标上优于现有先进方法1.3%至5.6%。研究成果为中文法律RAG提供了专用基准、实用框架与实证洞见。代码与数据已开源。
原文摘要 · Abstract (English)
Retrieval-Augmented Generation (RAG) has emerged as a promising technology for legal document consultation, yet its application in Chinese legal scenarios faces two key limitations: existing benchmarks lack specialized support for joint retriever-generator evaluation, and mainstream RAG systems often fail to accommodate the structured nature of legal provisions. To address these gaps, this study advances two core contributions: First, we constructed the Legal-DC benchmark dataset, comprising 480 legal documents (covering areas such as market regulation and contract management) and 2,475 refined question-answer pairs, each annotated with clause-level references, filling the gap for specialized evaluation resources in Chinese legal RAG. Second, we propose the LegRAG framework, which integrates legal adaptive indexing (clause-boundary segmentation) with a dual-path self-reflection mechanism to ensure clause integrity while enhancing answer accuracy. Third, we introduce automated evaluation methods for large language models to meet the high-reliability demands of legal retrieval scenarios. LegRAG outperforms existing state-of-the-art methods by 1.3% to 5.6% across key evaluation metrics. This research provides a specialized benchmark, practical framework, and empirical insights to advance the development of Chinese legal RAG systems. Our code and data are available at https://github.com/legal-dc/Legal-DC.
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