Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents
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 special...
| Authors | Yaocong Li, Qiang Lan, Leihan Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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