  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
