# A Survey of Classification Tasks and Approaches for Legal Contracts

arXiv: https://arxiv.org/abs/2507.21108 · HTML: https://mlsanity.com/paper/2507.21108/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 50 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://mlsanity.com/paper/2411.03350/) (2024)
- [LEGAL-BERT: The Muppets straight out of Law School](https://mlsanity.com/paper/2010.02559/) (2020)
- [LLaMA Pro: Progressive LLaMA with Block Expansion](https://mlsanity.com/paper/2401.02415/) (2024)
- [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://mlsanity.com/paper/2103.13084/) (2021)
- [CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service](https://mlsanity.com/paper/1805.01217/) (2018)
- [LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain](https://mlsanity.com/paper/2301.13126/) (2023)
- [MultiLegalPile: A 689GB Multilingual Legal Corpus](https://mlsanity.com/paper/2306.02069/) (2023)
- [Metacognitive Prompting Improves Understanding in Large Language Models](https://mlsanity.com/paper/2308.05342/) (2023)
- [LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development](https://mlsanity.com/paper/2305.07507/) (2023)
- [Fast Vocabulary Transfer for Language Model Compression](https://mlsanity.com/paper/2402.09977/) (2024)
- [Pre-trained Language Models for the Legal Domain: A Case Study on Indian Law](https://mlsanity.com/paper/2209.06049/) (2022)
- [ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmark](https://mlsanity.com/paper/2304.12202/) (2023)
- [LeSICiN: A Heterogeneous Graph-based Approach for Automatic Legal Statute Identification from Indian Legal Documents](https://mlsanity.com/paper/2112.14731/) (2021)
- [Multi-word Tokenization for Sequence Compression](https://mlsanity.com/paper/2402.09949/) (2024)
- [Task Compass: Scaling Multi-task Pre-training with Task Prefix](https://mlsanity.com/paper/2210.06277/) (2022)
- [DALE: Generative Data Augmentation for Low-Resource Legal NLP](https://mlsanity.com/paper/2310.15799/) (2023)
- [Agent-Specific Deontic Modality Detection in Legal Language](https://mlsanity.com/paper/2211.12752/) (2022)
- [Linear Classifier: An Often-Forgotten Baseline for Text Classification](https://mlsanity.com/paper/2306.07111/) (2023)

## Built on by (most cited first, 6 of 6)

- [Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification](https://mlsanity.com/paper/2508.07849/) (2025)
- [When Routine Chats Turn Toxic: Unintended Long-Term State Poisoning in Personalized Agents](https://mlsanity.com/paper/2605.06731/) (2026)
- [Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts](https://mlsanity.com/paper/2604.17674/) (2026)
- [LAUKIN: A Multi-jurisdictional Common Law Contract Dataset](https://mlsanity.com/paper/2606.13184/) (2026)
- [ContraVis: Evidence-Grounded Visual Analytics for Contradiction Review in Legal Contracts](https://mlsanity.com/paper/2609.27014/) (2026)
- [LAURA: Knowledge Distillation for Interpretable Ambiguous Clause Identification in Legal Contracts](https://mlsanity.com/paper/2609.36707/) (2026)

## Abstract (arXiv)

Given the large size and volumes of contracts and their underlying inherent complexity, manual reviews become inefficient and prone to errors, creating a clear need for automation. Automatic Legal Contract Classification (LCC) revolutionizes the way legal contracts are analyzed, offering substantial improvements in speed, accuracy, and accessibility. This survey delves into the challenges of automatic LCC and a detailed examination of key tasks, datasets, and methodologies. We identify seven classification tasks within LCC, and review fourteen datasets related to English-language contracts, including public, proprietary, and non-public sources. We also introduce a methodology taxonomy for LCC, categorized into Traditional Machine Learning, Deep Learning, and Transformer-based approaches. Additionally, the survey discusses evaluation techniques and highlights the best-performing results from the reviewed studies. By providing a thorough overview of current methods and their limitations, this survey suggests future research directions to improve the efficiency, accuracy, and scalability of LCC. As the first comprehensive survey on LCC, it aims to support legal NLP researchers and practitioners in improving legal processes, making legal information more accessible, and promoting a more informed and equitable society.