# The Cambridge Law Corpus: A Dataset for Legal AI Research

arXiv: https://arxiv.org/abs/2309.12269 · HTML: https://mlsanity.com/paper/2309.12269/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [MultiLegalPile: A 689GB Multilingual Legal Corpus](https://mlsanity.com/paper/2306.02069/) (2023)
- [LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development](https://mlsanity.com/paper/2305.07507/) (2023)
- [A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction](https://mlsanity.com/paper/2206.05224/) (2022)
- [Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights](https://mlsanity.com/paper/1912.10819/) (2019)
- [Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models](https://mlsanity.com/paper/1506.03784/) (2015)
- [P'olya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler](https://mlsanity.com/paper/1704.03581/) (2017)

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

- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://mlsanity.com/paper/2405.20362/) (2024)
- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [LLM vs. Lawyers: Identifying a Subset of Summary Judgments in a Large UK Case Law Dataset](https://mlsanity.com/paper/2403.04791/) (2024)
- [LegiLM: A Fine-Tuned Legal Language Model for Data Compliance](https://mlsanity.com/paper/2409.13721/) (2024)
- [The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal](https://mlsanity.com/paper/2409.08098/) (2024)
- [Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment](https://mlsanity.com/paper/2405.12910/) (2024)
- [Computational Law: Datasets, Benchmarks, and Ontologies](https://mlsanity.com/paper/2503.04305/) (2025)
- [LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation](https://mlsanity.com/paper/2505.19667/) (2025)
- [SinhaLegal: A Benchmark Corpus for Information Extraction and Analysis in Sinhala Legislative Texts](https://mlsanity.com/paper/2603.04854/) (2026)
- [PYTHEN: A Flexible Framework for Legal Reasoning in Python](https://mlsanity.com/paper/2603.15317/) (2026)
- [Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning](https://mlsanity.com/paper/2606.27069/) (2026)
- [Gated Against One Model, Open to the Next: Option-Only Solvability in Legal Multiple-Choice Benchmarks](https://mlsanity.com/paper/2608.15428/) (2026)
- [The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings](https://mlsanity.com/paper/2604.15460/) (2026)
- [How Much is a Human Right Worth? ECtHR-NPD: A Benchmark for Predicting Non-Pecuniary Damage Awards](https://mlsanity.com/paper/2609.18908/) (2026)

## Abstract (arXiv)

We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the corpus includes cases as old as the 16th century. This paper presents the first release of the corpus, containing the raw text and meta-data. Together with the corpus, we provide annotations on case outcomes for 638 cases, done by legal experts. Using our annotated data, we have trained and evaluated case outcome extraction with GPT-3, GPT-4 and RoBERTa models to provide benchmarks. We include an extensive legal and ethical discussion to address the potentially sensitive nature of this material. As a consequence, the corpus will only be released for research purposes under certain restrictions.