# LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation

arXiv: https://arxiv.org/abs/2505.23832 · HTML: https://mlsanity.com/paper/2505.23832/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 37 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 27 of 27)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Transformer Memory as a Differentiable Search Index](https://mlsanity.com/paper/2202.06991/) (2022)
- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://mlsanity.com/paper/2405.20362/) (2024)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://mlsanity.com/paper/2204.10628/) (2022)
- [A Neural Corpus Indexer for Document Retrieval](https://mlsanity.com/paper/2206.02743/) (2022)
- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [Learning to Rank in Generative Retrieval](https://mlsanity.com/paper/2306.15222/) (2023)
- [Scalable and Effective Generative Information Retrieval](https://mlsanity.com/paper/2311.09134/) (2023)
- [LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset](https://mlsanity.com/paper/2310.17609/) (2023)
- [DSI++: Updating Transformer Memory with New Documents](https://mlsanity.com/paper/2212.09744/) (2022)
- [Yes, BM25 is a Strong Baseline for Legal Case Retrieval](https://mlsanity.com/paper/2105.05686/) (2021)
- [Planning Ahead in Generative Retrieval: Guiding Autoregressive Generation through Simultaneous Decoding](https://mlsanity.com/paper/2404.14600/) (2024)
- [THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval](https://mlsanity.com/paper/2305.06812/) (2023)
- [Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation](https://mlsanity.com/paper/2406.19760/) (2024)
- [textttMixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity](https://mlsanity.com/paper/2407.10691/) (2024)
- [MUSER: A Multi-View Similar Case Retrieval Dataset](https://mlsanity.com/paper/2310.15602/) (2023)
- [Exploring the Practicality of Generative Retrieval on Dynamic Corpora](https://mlsanity.com/paper/2305.18952/) (2023)
- [THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment](https://mlsanity.com/paper/2012.13102/) (2020)
- [Data-efficient End-to-end Information Extraction for Statistical Legal Analysis](https://mlsanity.com/paper/2211.01692/) (2022)

## Built on by (most cited first, 5 of 5)

- [Evaluating Legal Reasoning Traces with Legal Issue Tree Rubrics](https://mlsanity.com/paper/2512.01020/) (2025)
- [AppellateGen: A Benchmark for Appellate Legal Judgment Generation](https://mlsanity.com/paper/2601.01331/) (2026)
- [Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA](https://mlsanity.com/paper/2604.06173/) (2026)
- [GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval](https://mlsanity.com/paper/2604.23779/) (2026)
- [UK-PRBENCH: A Paragraph-Level Precedent Retrieval Benchmark for United Kingdom Case Law](https://mlsanity.com/paper/2609.24613/) (2026)

## Abstract (arXiv)

Legal Case Retrieval (LCR), which retrieves relevant cases from a query case, is a fundamental task for legal professionals in research and decision-making. However, existing studies on LCR face two major limitations. First, they are evaluated on relatively small-scale retrieval corpora (e.g., 100-55K cases) and use a narrow range of criminal query types, which cannot sufficiently reflect the complexity of real-world legal retrieval scenarios. Second, their reliance on embedding-based or lexical matching methods often results in limited representations and legally irrelevant matches. To address these issues, we present: (1) LEGAR BENCH, the first large-scale Korean LCR benchmark, covering 411 diverse crime types in queries over 1.2M candidate cases; and (2) LegalSearchLM, a retrieval model that performs legal element reasoning over the query case and directly generates content containing those elements, grounded in the target cases through constrained decoding. Experimental results show that LegalSearchLM outperforms baselines by 6-20% on LEGAR BENCH, achieving state-of-the-art performance. It also demonstrates strong generalization to out-of-domain cases, outperforming naive generative models trained on in-domain data by 15%.