# Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval

arXiv: https://arxiv.org/abs/2404.00947 · HTML: https://mlsanity.com/paper/2404.00947/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2024.

## Built on by (most cited first, 6 of 6)

- [CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation](https://mlsanity.com/paper/2502.17943/) (2025)
- [IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval](https://mlsanity.com/paper/2511.00268/) (2025)
- [When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance](https://mlsanity.com/paper/2507.07748/) (2025)
- [NOWJ@COLIEE 2025: A Multi-stage Framework Integrating Embedding Models and Large Language Models for Legal Retrieval and Entailment](https://mlsanity.com/paper/2509.08025/) (2025)
- [ILSIC: Corpora for Identifying Indian Legal Statutes from Queries by Laypeople](https://mlsanity.com/paper/2602.00881/) (2026)
- [ABAI at COLIEE 2026 Task 1: Multi-Stage Retrieval with GraphRAG-Enhanced Meta-Learning, and a Post-Hoc Study of the Cross-Validation-to-Test Gap](https://mlsanity.com/paper/2609.26237/) (2026)

## Abstract (arXiv)

Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system. As an annually well-known international competition, COLIEE aims to advance the development of state-of-the-art retrieval models for legal texts. This paper elaborates on the methodology employed by the TQM team in COLIEE2024.Specifically, we explored various lexical matching and semantic retrieval models, with a focus on enhancing the understanding of case relevance. Additionally, we endeavor to integrate various features using the learning-to-rank technique. Furthermore, fine heuristic pre-processing and post-processing methods have been proposed to mitigate irrelevant information. Consequently, our methodology achieved remarkable performance in COLIEE2024, securing first place in Task 1 and third place in Task 3. We anticipate that our proposed approach can contribute valuable insights to the advancement of legal retrieval technology.