# Generative Chinese Statute Retrieval

arXiv: https://arxiv.org/abs/2607.11109 · HTML: https://mlsanity.com/paper/2607.11109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Builds on (most cited first, 13 of 13)

- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [A Neural Corpus Indexer for Document Retrieval](https://mlsanity.com/paper/2206.02743/) (2022)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer](https://mlsanity.com/paper/2208.09257/) (2022)
- [JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System](https://mlsanity.com/paper/2503.14258/) (2025)
- [THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval](https://mlsanity.com/paper/2305.06812/) (2023)
- [Attentive Deep Neural Networks for Legal Document Retrieval](https://mlsanity.com/paper/2212.13899/) (2022)
- [An Intent Taxonomy of Legal Case Retrieval](https://mlsanity.com/paper/2307.13298/) (2023)
- [Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization](https://mlsanity.com/paper/2605.02011/) (2026)
- [QBR: A Question-Bank-Based Approach to Fine-Grained Legal Knowledge Retrieval for the General Public](https://mlsanity.com/paper/2505.04883/) (2025)

## Abstract (arXiv)

Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory language. In this paper, we propose GCSR, a generative statute retrieval framework that reformulates statute retrieval as a sequence generation problem and internalizes statutory knowledge into a generative model. Specifically, we propose a multi-granularity structured docid that encodes legal hierarchy and semantic information, together with a multi-task training strategy. Experiments show that GCSR consistently outperforms strong sparse, dense, and legal-domain baselines. Our results demonstrate the effectiveness of generative retrieval for statute retrieval and highlight its potential for broader legal information access and downstream legal reasoning tasks.