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:
this https URL

为了填补现有法律查询数据集对非专业用户提出的实际查询的不足，本文引入了 STAtute Retrieval Dataset（STARD），该数据集由 1543 个来自现实世界法律咨询的查询案例和 55348 个候选法规文章组成，并通过对各种检索基线模型进行综合评估，揭示了现有检索方法在非专业用户提交的实际查询中的不足，为进一步探索和研究提供了必要性。

STARD：一个包括非专业人员发出的真实查询的中文法规检索数据集

STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by  Non-professionals

Seeking legal advice is often expensive. Recent advancements in machine
learning for solving complex problems can be leveraged to help make legal
services more accessible to the public. However, real-life applications
encounter significant challenges. State-of-the-art language models are growing
increasingly large, making parameter-efficient learning increasingly important.
Unfortunately, parameter-efficient methods perform poorly with small amounts of
data, which are common in the legal domain (where data labelling costs are
high). To address these challenges, we propose parameter-efficient legal domain
adaptation, which uses vast unsupervised legal data from public legal forums to
perform legal pre-training. This method exceeds or matches the fewshot
performance of existing models such as LEGAL-BERT on various legal tasks while
tuning only approximately 0.1% of model parameters. Additionally, we show that
our method can achieve calibration comparable to existing methods across
several tasks. To the best of our knowledge, this work is among the first to
explore parameter-efficient methods of tuning language models in the legal
domain.

本文提出了一种参数高效的法律领域适应方法，该方法使用公共法律论坛的海量无监督法律数据进行法律预训练，成功地在各种法律任务上超过或匹配现有模型的低样本学习性能，同时只调整了约 0.1％的模型参数，达到与现有方法相当的校准效果。