# Prototype-Based Interpretability for Legal Citation Prediction

arXiv: https://arxiv.org/abs/2305.16490 · HTML: https://mlsanity.com/paper/2305.16490/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 21 of 21)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [Principles and Practice of Explainable Machine Learning](https://mlsanity.com/paper/2009.11698/) (2020)
- [ProtGNN: Towards Self-Explaining Graph Neural Networks](https://mlsanity.com/paper/2112.00911/) (2021)
- [Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models](https://mlsanity.com/paper/2110.07736/) (2021)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network](https://mlsanity.com/paper/1905.03969/) (2019)
- [LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents](https://mlsanity.com/paper/2112.14731/) (2021)
- [Feature Removal Is a Unifying Principle for Model Explanation Methods](https://mlsanity.com/paper/2011.03623/) (2020)
- [Evaluating Explainable AI on a Multi-Modal Medical Imaging Task: Can Existing Algorithms Fulfill Clinical Requirements?](https://mlsanity.com/paper/2203.06487/) (2022)
- [Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis](https://mlsanity.com/paper/2104.09420/) (2021)
- [Context-Aware Legal Citation Recommendation using Deep Learning](https://mlsanity.com/paper/2106.10776/) (2021)
- [Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models](https://mlsanity.com/paper/2112.07165/) (2021)
- [Interpretable Low-Resource Legal Decision Making](https://mlsanity.com/paper/2201.01164/) (2022)
- [The relative influences of government funding and international collaboration on citation impact](https://mlsanity.com/paper/1712.04659/) (2017)
- [Prediction Methods and Applications in the Science of Science: A Survey](https://mlsanity.com/paper/2008.03640/) (2020)

## Built on by (most cited first, 9 of 9)

- [Evaluating AI for Law: Bridging the Gap with Open-Source Solutions](https://mlsanity.com/paper/2404.12349/) (2024)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [CiteBART: Learning to Generate Citations for Local Citation Recommendation](https://mlsanity.com/paper/2412.17534/) (2024)
- [Coupling Local Context and Global Semantic Prototypes via a Hierarchical Architecture for Rhetorical Roles Labeling](https://mlsanity.com/paper/2603.03856/) (2026)
- [NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models](https://mlsanity.com/paper/2606.15646/) (2026)
- [Legal Experts Disagree With Rationale Extraction Techniques for Explaining ECtHR Case Outcome Classification](https://mlsanity.com/paper/2601.12419/) (2026)
- [NyayaMind- A Framework for Transparent Legal Reasoning and Judgment Prediction in the Indian Legal System](https://mlsanity.com/paper/2604.09069/) (2026)
- [Seeing the Needle in the Haystack: Towards Weakly-Supervised Log Instance Anomaly Localization via Counterfactual Perturbation](https://mlsanity.com/paper/2605.10988/) (2026)
- [A Four-Axis Trustworthiness Benchmark for LLM-as-Judge in Principle-Based Regulation](https://mlsanity.com/paper/2608.14329/) (2026)

## Abstract (arXiv)

Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact. In high-stakes decision making areas such as law, experts often require interpretability for automatic systems to be utilized in practical settings. In this work, we attempt to address these requirements applied to the important problem of legal citation prediction (LCP). We design the task with parallels to the thought-process of lawyers, i.e., with reference to both precedents and legislative provisions. After initial experimental results, we refine the target citation predictions with the feedback of legal experts. Additionally, we introduce a prototype architecture to add interpretability, achieving strong performance while adhering to decision parameters used by lawyers. Our study builds on and leverages the state-of-the-art language processing models for law, while addressing vital considerations for high-stakes tasks with practical societal impact.