# Natural Language Processing in the Legal Domain

arXiv: https://arxiv.org/abs/2302.12039 · HTML: https://mlsanity.com/paper/2302.12039/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 95 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Efficient Transformers: A Survey](https://mlsanity.com/paper/2009.06732/) (2020)
- [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer](https://mlsanity.com/paper/2109.00904/) (2021)
- [GPT Takes the Bar Exam](https://mlsanity.com/paper/2212.14402/) (2022)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [LexNLP: Natural language processing and information extraction for legal and regulatory texts](https://mlsanity.com/paper/1806.03688/) (2018)
- [The Diminishing Returns of Masked Language Models to Science](https://mlsanity.com/paper/2205.11342/) (2022)
- [GPT as Knowledge Worker: A Zero-Shot Evaluation of (AI)CPA Capabilities](https://mlsanity.com/paper/2301.04408/) (2023)
- [Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany](https://mlsanity.com/paper/2101.11284/) (2021)
- [Large Language Models as Corporate Lobbyists](https://mlsanity.com/paper/2301.01181/) (2023)
- [Complex Societies and the Growth of the Law](https://mlsanity.com/paper/2005.07646/) (2020)
- [What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018](https://mlsanity.com/paper/1906.04588/) (2019)
- [Power laws in citation distributions: Evidence from Scopus](https://mlsanity.com/paper/1402.3890/) (2014)
- [Human-in-the-loop Machine Learning: A Macro-Micro Perspective](https://mlsanity.com/paper/2202.10564/) (2022)

## Built on by (most cited first, 30 of 38)

- [A Short Survey of Viewing Large Language Models in Legal Aspect](https://mlsanity.com/paper/2303.09136/) (2023)
- [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023)
- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation](https://mlsanity.com/paper/2403.02959/) (2024)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Let's (not) just put things in Context: Test-Time Training for Long-Context LLMs](https://mlsanity.com/paper/2512.13898/) (2025)
- [From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization](https://mlsanity.com/paper/2410.13961/) (2024)
- [Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration](https://mlsanity.com/paper/2410.02507/) (2024)
- [Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?](https://mlsanity.com/paper/2310.14880/) (2023)
- [SwiLTra-Bench: The Swiss Legal Translation Benchmark](https://mlsanity.com/paper/2503.01372/) (2025)
- [Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs](https://mlsanity.com/paper/2503.07384/) (2025)
- [LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in English](https://mlsanity.com/paper/2410.09527/) (2024)
- [Anonymity at Risk? Assessing Re-Identification Capabilities of Large Language Models](https://mlsanity.com/paper/2308.11103/) (2023)
- [LexDrafter: Terminology Drafting for Legislative Documents using Retrieval Augmented Generation](https://mlsanity.com/paper/2403.16295/) (2024)
- [Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey](https://mlsanity.com/paper/2404.00990/) (2024)
- [It Cannot Be Right If It Was Written by AI: On Lawyers' Preferences of Documents Perceived as Authored by an LLM vs a Human](https://mlsanity.com/paper/2407.06798/) (2024)
- [CaseSumm: A Large-Scale Dataset for Long-Context Summarization from U.S. Supreme Court Opinions](https://mlsanity.com/paper/2501.00097/) (2024)
- [LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval](https://mlsanity.com/paper/2504.16121/) (2025)
- [Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing](https://mlsanity.com/paper/2510.08111/) (2025)
- [Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments](https://mlsanity.com/paper/2506.00694/) (2025)
- [AutoSpec: An Agentic Framework for Automatically Drafting Patent Specification](https://mlsanity.com/paper/2509.19640/) (2025)
- [Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.09623/) (2024)
- [Identifying Legal Holdings with LLMs: A Systematic Study of Performance, Scale, and Memorization](https://mlsanity.com/paper/2505.02172/) (2025)
- [RISC: Generating Realistic Synthetic Bilingual Insurance Contract](https://mlsanity.com/paper/2304.04212/) (2023)
- [TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce](https://mlsanity.com/paper/2312.05103/) (2023)
- [No Argument Left Behind: Overlapping Chunks for Faster Processing of Arbitrarily Long Legal Texts](https://mlsanity.com/paper/2410.19184/) (2024)
- [LegalViz: Legal Text Visualization by Text To Diagram Generation](https://mlsanity.com/paper/2502.06147/) (2025)
- [ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation](https://mlsanity.com/paper/2508.17234/) (2025)
- [Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA](https://mlsanity.com/paper/2604.06173/) (2026)
- [Building Legal Reward Models for Grounding and Abstention](https://mlsanity.com/paper/2609.14739/) (2026)

## Abstract (arXiv)

We summarize the current state of the field of NLP & Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a nearly complete corpus of nearly one thousand NLP & Law related papers published between 2013-2024. Our analysis highlights several major trends. Namely, we document an increasing number of papers written, tasks undertaken, and languages covered over the course of the past decade. We observe an increase in the sophistication of the methods which researchers deployed in this applied context. Legal NLP is beginning to match not only the methodological sophistication of general NLP but also the professional standards of data availability and code reproducibility observed within the broader scientific community. We believe all of these trends bode well for the future of the field and point to an exciting next phase for the Legal NLP community.