# Case law retrieval: problems, methods, challenges and evaluations in the last 20 years

arXiv: https://arxiv.org/abs/2202.07209 · HTML: https://mlsanity.com/paper/2202.07209/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 140 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Open Domain Web Keyphrase Extraction Beyond Language Modeling](https://mlsanity.com/paper/1911.02671/) (2019)
- [Patent Retrieval: A Literature Review](https://mlsanity.com/paper/1701.00324/) (2017)
- [Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similarity](https://mlsanity.com/paper/2007.03225/) (2020)
- [Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network](https://mlsanity.com/paper/1703.05320/) (2017)
- [Network Analysis in the Legal Domain: A complex model for European Union legal sources](https://mlsanity.com/paper/1501.05237/) (2015)
- [Lexical-Morphological Modeling for Legal Text Analysis](https://mlsanity.com/paper/1609.00799/) (2016)

## Built on by (most cited first, 10 of 10)

- [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)
- [L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Audit](https://mlsanity.com/paper/2509.00761/) (2025)
- [LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset](https://mlsanity.com/paper/2403.04791/) (2024)
- [An Intent Taxonomy of Legal Case Retrieval](https://mlsanity.com/paper/2307.13298/) (2023)
- [Citation Recommendation on Scholarly Legal Articles](https://mlsanity.com/paper/2311.05902/) (2023)
- [Evaluating GPT-3.5's Awareness and Summarization Abilities for European Constitutional Texts with Shared Topics](https://mlsanity.com/paper/2401.14524/) (2024)
- [LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents](https://mlsanity.com/paper/2509.02241/) (2025)
- [LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval](https://mlsanity.com/paper/2601.17692/) (2026)
- [LegalPincite: Multi-level Legal Information Retrieval Dataset](https://mlsanity.com/paper/2608.03756/) (2026)

## Abstract (arXiv)

Case law retrieval is the retrieval of judicial decisions relevant to a legal question. Case law retrieval comprises a significant amount of a lawyer's time, and is important to ensure accurate advice and reduce workload. We survey methods for case law retrieval from the past 20 years and outline the problems and challenges facing evaluation of case law retrieval systems going forward. Limited published work has focused on improving ranking in ad-hoc case law retrieval. But there has been significant work in other areas of case law retrieval, and legal information retrieval generally. This is likely due to legal search providers being unwilling to give up the secrets of their success to competitors. Most evaluations of case law retrieval have been undertaken on small collections and focus on related tasks such as question-answer systems or recommender systems. Work has not focused on Cranfield style evaluations and baselines of methods for case law retrieval on publicly available test collections are not present. This presents a major challenge going forward. But there are reasons to question the extent of this problem, at least in a commercial setting. Without test collections to baseline approaches it cannot be known whether methods are promising. Works by commercial legal search providers show the effectiveness of natural language systems as well as query expansion for case law retrieval. Machine learning is being applied to more and more legal search tasks, and undoubtedly this represents the future of case law retrieval.