# AutoLAW: Augmented Legal Reasoning through Legal Precedent Prediction

arXiv: https://arxiv.org/abs/2106.16034 · HTML: https://mlsanity.com/paper/2106.16034/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 30 Jun 2021.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [LEGAL-BERT: The Muppets straight out of Law School](https://mlsanity.com/paper/2010.02559/) (2020)
- [Learning to Predict Charges for Criminal Cases with Legal Basis](https://mlsanity.com/paper/1707.09168/) (2017)
- [Charge-Based Prison Term Prediction with Deep Gating Network](https://mlsanity.com/paper/1908.11521/) (2019)
- [Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data](https://mlsanity.com/paper/2003.11563/) (2020)

## Built on by (most cited first, 14 of 14)

- [PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2402.07867/) (2024)
- [A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challenges](https://mlsanity.com/paper/2204.04859/) (2022)
- [Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications](https://mlsanity.com/paper/2411.14110/) (2024)
- [CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation](https://mlsanity.com/paper/2406.17186/) (2024)
- [The Law and NLP: Bridging Disciplinary Disconnects](https://mlsanity.com/paper/2310.14346/) (2023)
- [LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based Retrieval](https://mlsanity.com/paper/2212.03222/) (2022)
- [POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models](https://mlsanity.com/paper/2505.06579/) (2025)
- [Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey](https://mlsanity.com/paper/2510.12178/) (2025)
- [LePaRD: A Large-Scale Dataset of Judges Citing Precedents](https://mlsanity.com/paper/2311.09356/) (2023)
- [UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.18652/) (2025)
- [Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitation](https://mlsanity.com/paper/2507.23229/) (2025)
- [A Two-Stage Architecture for NDA Analysis: LLM-based Segmentation and Transformer-based Clause Classification](https://mlsanity.com/paper/2603.09990/) (2026)
- [When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Models](https://mlsanity.com/paper/2608.19529/) (2026)
- [Walking the Embedding Space: Datastore Extraction from Multimodal RAG](https://mlsanity.com/paper/2610.01871/) (2026)

## Abstract (arXiv)

This paper demonstrate how NLP can be used to address an unmet need of the legal community and increase access to justice. The paper introduces Legal Precedent Prediction (LPP), the task of predicting relevant passages from precedential court decisions given the context of a legal argument. To this end, the paper showcases a BERT model, trained on 530,000 examples of legal arguments made by U.S. federal judges, to predict relevant passages from precedential court decisions given the context of a legal argument. In 96% of unseen test examples the correct target passage is among the top-10 predicted passages. The same model is able to predict relevant precedent given a short summary of a complex and unseen legal brief, predicting the precedent that was actually cited by the brief's co-author, former U.S. Solicitor General and current U.S. Supreme Court Justice Elena Kagan.