mlsanityData to 14 Oct 2026

AutoLAW: Augmented Legal Reasoning through Legal Precedent Prediction

arXiv 2106.16034 ↗First posted 30 Jun 2021cs.CLcited by 14

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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.

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

7 of 22 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Neural Legal Judgment Prediction in Englishcited by 209
  3. 2020How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligencecited by 129
  4. 2020LEGAL-BERT: The Muppets straight out of Law Schoolcited by 111
  5. 2017Learning to Predict Charges for Criminal Cases with Legal Basiscited by 61
  6. 2019Charge-Based Prison Term Prediction with Deep Gating Networkcited by 31
  7. 2020Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Datacited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 3; 2025: 4; 2026: 3.

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  1. 2024PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Modelscited by 267
  2. 2022A Survey on Legal Judgment Prediction: Datasets, Metrics, Models and Challengescited by 50
  3. 2024Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applicationscited by 37
  4. 2024CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generationcited by 18
  5. 2023The Law and NLP: Bridging Disciplinary Disconnectscited by 8
  6. 2022LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from Short to Long Contexts and for Implication-Based Retrievalcited by 6
  7. 2025POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Modelscited by 6
  8. 2025Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a surveycited by 5
  9. 2023LePaRD: A Large-Scale Dataset of Judges Citing Precedentscited by 3
  10. 2025UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generationcited by 3
  11. 2025Fine-Grained Privacy Extraction from Retrieval-Augmented Generation Systems via Knowledge Asymmetry Exploitationcited by 2
  12. 2026A Two-Stage Architecture for NDA Analysis: LLM-based Segmentation and Transformer-based Clause Classificationcited by 0
2 more
  1. 2026When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Modelscited by 0
  2. 2026Walking the Embedding Space: Datastore Extraction from Multimodal RAGcited by 0