# MUSER: A Multi-View Similar Case Retrieval Dataset

arXiv: https://arxiv.org/abs/2310.15602 · HTML: https://mlsanity.com/paper/2310.15602/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 24 Oct 2023.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain](https://mlsanity.com/paper/1911.08962/) (2019)
- [Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model](https://mlsanity.com/paper/2009.14083/) (2020)
- [Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction](https://mlsanity.com/paper/2207.04182/) (2022)
- [THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval](https://mlsanity.com/paper/2305.06812/) (2023)
- [THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment](https://mlsanity.com/paper/2305.06817/) (2023)
- [An Intent Taxonomy of Legal Case Retrieval](https://mlsanity.com/paper/2307.13298/) (2023)
- [THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment](https://mlsanity.com/paper/2012.13102/) (2020)
- [Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval](https://mlsanity.com/paper/2210.05188/) (2022)

## Built on by (most cited first, 8 of 8)

- [STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals](https://mlsanity.com/paper/2406.15313/) (2024)
- [J&H: Evaluating the Robustness of Large Language Models Under Knowledge-Injection Attacks in Legal Domain](https://mlsanity.com/paper/2503.18360/) (2025)
- [LegalSearchLM: Rethinking Legal Case Retrieval as Legal Elements Generation](https://mlsanity.com/paper/2505.23832/) (2025)
- [LLM-based Embedders for Prior Case Retrieval](https://mlsanity.com/paper/2507.18455/) (2025)
- [Unlocking Multi-View Insights in Knowledge-Dense Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.12879/) (2024)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [Labeling Case Similarity based on Co-Citation of Legal Articles in Judgment Documents with Empirical Dispute-Based Evaluation](https://mlsanity.com/paper/2504.20323/) (2025)
- [UK-PRBENCH: A Paragraph-Level Precedent Retrieval Benchmark for United Kingdom Case Law](https://mlsanity.com/paper/2609.24613/) (2026)

## Abstract (arXiv)

Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. However, existing SCR datasets only focus on the fact description section when judging the similarity between cases, ignoring other valuable sections (e.g., the court's opinion) that can provide insightful reasoning process behind. Furthermore, the case similarities are typically measured solely by the textual semantics of the fact descriptions, which may fail to capture the full complexity of legal cases from the perspective of legal knowledge. In this work, we present MUSER, a similar case retrieval dataset based on multi-view similarity measurement and comprehensive legal element with sentence-level legal element annotations. Specifically, we select three perspectives (legal fact, dispute focus, and law statutory) and build a comprehensive and structured label schema of legal elements for each of them, to enable accurate and knowledgeable evaluation of case similarities. The constructed dataset originates from Chinese civil cases and contains 100 query cases and 4,024 candidate cases. We implement several text classification algorithms for legal element prediction and various retrieval methods for retrieving similar cases on MUSER. The experimental results indicate that incorporating legal elements can benefit the performance of SCR models, but further efforts are still required to address the remaining challenges posed by MUSER. The source code and dataset are released at https://github.com/THUlawtech/MUSER.