STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals
This addresses a gap in legal AI for the general public by focusing on real, non-professional queries, though it is incremental as it primarily introduces a new dataset.
The authors tackled the problem of statute retrieval for non-professional queries by introducing STARD, a Chinese dataset with 1,543 real-world queries and 55,348 candidate articles, and found that existing retrieval methods perform poorly, with the best achieving only a Recall@100 of 0.907.
Statute retrieval aims to find relevant statutory articles for specific queries. This process is the basis of a wide range of legal applications such as legal advice, automated judicial decisions, legal document drafting, etc. Existing statute retrieval benchmarks focus on formal and professional queries from sources like bar exams and legal case documents, thereby neglecting non-professional queries from the general public, which often lack precise legal terminology and references. To address this gap, we introduce the STAtute Retrieval Dataset (STARD), a Chinese dataset comprising 1,543 query cases collected from real-world legal consultations and 55,348 candidate statutory articles. Unlike existing statute retrieval datasets, which primarily focus on professional legal queries, STARD captures the complexity and diversity of real queries from the general public. Through a comprehensive evaluation of various retrieval baselines, we reveal that existing retrieval approaches all fall short of these real queries issued by non-professional users. The best method only achieves a Recall@100 of 0.907, suggesting the necessity for further exploration and additional research in this area. All the codes and datasets are available at: https://github.com/oneal2000/STARD/tree/main