# Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs

arXiv: https://arxiv.org/abs/2410.06581 · HTML: https://mlsanity.com/paper/2410.06581/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2024.

## Built on by (most cited first, 11 of 11)

- [GuRE:Generative Query REwriter for Legal Passage Retrieval](https://mlsanity.com/paper/2505.12950/) (2025)
- [LRAGE: Legal Retrieval Augmented Generation Evaluation Tool](https://mlsanity.com/paper/2504.01840/) (2025)
- [Towards Trustworthy Legal AI through LLM Agents and Formal Reasoning](https://mlsanity.com/paper/2511.21033/) (2025)
- [ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework](https://mlsanity.com/paper/2506.18768/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [Kernel Affine Hull Machines as Compute-Efficient Encoders for Frozen Semantic Spaces](https://mlsanity.com/paper/2605.02950/) (2026)
- [LegalWorld: A Life-Cycle Interactive Environment for Legal Agents](https://mlsanity.com/paper/2606.18728/) (2026)
- [Legal Document Summarization: Enhancing Judicial Efficiency through Automation Detection](https://mlsanity.com/paper/2507.18952/) (2025)
- [PL-CA: A Parametric Legal Case Augmentation Framework](https://mlsanity.com/paper/2509.06356/) (2025)
- [STELLA: Self-Reflective Terminology-Aware Framework for Building an Aerospace Information Retrieval Benchmark](https://mlsanity.com/paper/2601.03496/) (2026)
- [GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval](https://mlsanity.com/paper/2604.23779/) (2026)

## Abstract (arXiv)

Legal case retrieval (LCR) aims to provide similar cases as references for a given fact description. This task is crucial for promoting consistent judgments in similar cases, effectively enhancing judicial fairness and improving work efficiency for judges. However, existing works face two main challenges for real-world applications: existing works mainly focus on case-to-case retrieval using lengthy queries, which does not match real-world scenarios; and the limited data scale, with current datasets containing only hundreds of queries, is insufficient to satisfy the training requirements of existing data-hungry neural models. To address these issues, we introduce an automated method to construct synthetic query-candidate pairs and build the largest LCR dataset to date, LEAD, which is hundreds of times larger than existing datasets. This data construction method can provide ample training signals for LCR models. Experimental results demonstrate that model training with our constructed data can achieve state-of-the-art results on two widely-used LCR benchmarks. Besides, the construction method can also be applied to civil cases and achieve promising results. The data and codes can be found in https://github.com/thunlp/LEAD.