# Logic Rules as Explanations for Legal Case Retrieval

arXiv: https://arxiv.org/abs/2403.01457 · HTML: https://mlsanity.com/paper/2403.01457/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 72 references that are in the map.
- First posted on arXiv 3 Mar 2024.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Towards Robust Interpretability with Self-Explaining Neural Networks](https://mlsanity.com/paper/1806.07538/) (2018)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [Logical Neural Networks](https://mlsanity.com/paper/2006.13155/) (2020)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [Legal Prompting: Teaching a Language Model to Think Like a Lawyer](https://mlsanity.com/paper/2212.01326/) (2022)
- [A Short Survey of Viewing Large Language Models in Legal Aspect](https://mlsanity.com/paper/2303.09136/) (2023)
- [Augmenting Neural Networks with First-order Logic](https://mlsanity.com/paper/1906.06298/) (2019)
- [Entropy-based Logic Explanations of Neural Networks](https://mlsanity.com/paper/2106.06804/) (2021)
- [Neural Logic Reasoning](https://mlsanity.com/paper/2008.09514/) (2020)
- [ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmark](https://mlsanity.com/paper/2304.12202/) (2023)
- [LexNLP: Natural language processing and information extraction for legal and regulatory texts](https://mlsanity.com/paper/1806.03688/) (2018)
- [Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models](https://mlsanity.com/paper/2201.10582/) (2022)
- [Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction](https://mlsanity.com/paper/2207.04182/) (2022)
- [Integrating Deep Learning with Logic Fusion for Information Extraction](https://mlsanity.com/paper/1912.03041/) (2019)
- [Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis](https://mlsanity.com/paper/2104.09420/) (2021)
- [Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach](https://mlsanity.com/paper/2312.09591/) (2023)
- [Self-explaining deep models with logic rule reasoning](https://mlsanity.com/paper/2210.07024/) (2022)
- [LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking](https://mlsanity.com/paper/2106.09795/) (2021)
- [Hier-SPCNet: A Legal Statute Hierarchy-based Heterogeneous Network for Computing Legal Case Document Similarity](https://mlsanity.com/paper/2007.03225/) (2020)
- [Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic](https://mlsanity.com/paper/2109.08927/) (2021)
- [Methods for Computing Legal Document Similarity: A Comparative Study](https://mlsanity.com/paper/2004.12307/) (2020)
- [Extending Logic Explained Networks to Text Classification](https://mlsanity.com/paper/2211.09732/) (2022)
- [Law Article-Enhanced Legal Case Matching: a Causal Learning Approach](https://mlsanity.com/paper/2210.11012/) (2022)

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

- [Towards Robust Legal Reasoning: Harnessing Logical LLMs in Law](https://mlsanity.com/paper/2502.17638/) (2025)
- [Legal Mathematical Reasoning with LLMs: Procedural Alignment through Two-Stage Reinforcement Learning](https://mlsanity.com/paper/2504.02590/) (2025)
- [Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey](https://mlsanity.com/paper/2404.00990/) (2024)
- [Towards Trustworthy Legal AI through LLM Agents and Formal Reasoning](https://mlsanity.com/paper/2511.21033/) (2025)
- [Effective In-Context Example Selection through Data Compression](https://mlsanity.com/paper/2405.11465/) (2024)

## Abstract (arXiv)

In this paper, we address the issue of using logic rules to explain the results from legal case retrieval. The task is critical to legal case retrieval because the users (e.g., lawyers or judges) are highly specialized and require the system to provide logical, faithful, and interpretable explanations before making legal decisions. Recently, research efforts have been made to learn explainable legal case retrieval models. However, these methods usually select rationales (key sentences) from the legal cases as explanations, failing to provide faithful and logically correct explanations. In this paper, we propose Neural-Symbolic enhanced Legal Case Retrieval (NS-LCR), a framework that explicitly conducts reasoning on the matching of legal cases through learning case-level and law-level logic rules. The learned rules are then integrated into the retrieval process in a neuro-symbolic manner. Benefiting from the logic and interpretable nature of the logic rules, NS-LCR is equipped with built-in faithful explainability. We also show that NS-LCR is a model-agnostic framework that can be plugged in for multiple legal retrieval models. To showcase NS-LCR's superiority, we enhance existing benchmarks by adding manually annotated logic rules and introducing a novel explainability metric using Large Language Models (LLMs). Our comprehensive experiments reveal NS-LCR's effectiveness for ranking, alongside its proficiency in delivering reliable explanations for legal case retrieval.