# Context-Aware Legal Citation Recommendation using Deep Learning

arXiv: https://arxiv.org/abs/2106.10776 · HTML: https://mlsanity.com/paper/2106.10776/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 20 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [On the State of the Art of Evaluation in Neural Language Models](https://mlsanity.com/paper/1707.05589/) (2017)
- [State-of-the-art Chinese Word Segmentation with Bi-LSTMs](https://mlsanity.com/paper/1808.06511/) (2018)
- [Network Analysis in the Legal Domain: A complex model for European Union legal sources](https://mlsanity.com/paper/1501.05237/) (2015)

## Built on by (most cited first, 12 of 12)

- [Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset](https://mlsanity.com/paper/2207.00220/) (2022)
- [Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevance](https://mlsanity.com/paper/2210.12437/) (2022)
- [Prototype-Based Interpretability for Legal Citation Prediction](https://mlsanity.com/paper/2305.16490/) (2023)
- [Debate-Feedback: A Multi-Agent Framework for Efficient Legal Judgment Prediction](https://mlsanity.com/paper/2504.05358/) (2025)
- [The Law and NLP: Bridging Disciplinary Disconnects](https://mlsanity.com/paper/2310.14346/) (2023)
- [SmoothHess: ReLU Network Feature Interactions via Stein's Lemma](https://mlsanity.com/paper/2311.00858/) (2023)
- [LePaRD: A Large-Scale Dataset of Judges Citing Precedents](https://mlsanity.com/paper/2311.09356/) (2023)
- [Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making](https://mlsanity.com/paper/2312.11507/) (2023)
- [NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models](https://mlsanity.com/paper/2606.15646/) (2026)
- [CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing](https://mlsanity.com/paper/2305.03508/) (2023)
- [Citation Recommendation on Scholarly Legal Articles](https://mlsanity.com/paper/2311.05902/) (2023)
- [When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Models](https://mlsanity.com/paper/2608.19529/) (2026)

## Abstract (arXiv)

Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions. In this paper, we develop a citation recommendation tool that can help improve efficiency in the process of opinion drafting. We train four types of machine learning models, including a citation-list based method (collaborative filtering) and three context-based methods (text similarity, BiLSTM and RoBERTa classifiers). Our experiments show that leveraging local textual context improves recommendation, and that deep neural models achieve decent performance. We show that non-deep text-based methods benefit from access to structured case metadata, but deep models only benefit from such access when predicting from context of insufficient length. We also find that, even after extensive training, RoBERTa does not outperform a recurrent neural model, despite its benefits of pretraining. Our behavior analysis of the RoBERTa model further shows that predictive performance is stable across time and citation classes.