The Cambridge Law Corpus: A Dataset for Legal AI Research
Open accessAbstract
We introduce the Cambridge Law Corpus (CLC), a corpus 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.
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Legal AI companies are subject to economic pressures to compete in an increasingly crowded market, pressures made more acute by the recent entry of previously copyrighted and proprietary data into the public domain.
“On the one hand, these companies are subject to economic pressures to compete in an increasingly crowded market (Ma et al. 2024), pressures made more acute by the recent entry of previously copyrighted and proprietary data into the public domain (Henderson et al. 2022; Östling et al. 2024; The Library Innovation Lab 2024).”
From Hallucination‐Free? Assessing the Reliability of Leading <scp>AI</scp> Legal Research Tools (2025) · cites this paper as Östling et al. 2024
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv · doi:10.48550/arxiv.1803.09010 · Cited by 2,474
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM · doi:10.1145/3458723 · Cited by 2,469 · see citing works
From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition
Morris, A. C., Maier, V., & Green, P. (2004) · Interspeech 2004 · doi:10.21437/interspeech.2004-668 · Cited by 290 · see citing works
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Hallucination‐Free? Assessing the Reliability of Leading <scp>AI</scp> Legal Research Tools
Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., & Ho, D. E. (2025) · Journal of Empirical Legal Studies · doi:10.1111/jels.12413 · Cited by 182 · see citing works
- Authors
- Östling, A., Sargeant, H., Xie, H., Bull, L., Terenin, A., Jonsson, L., Magnusson, M., & Steffek, F.
- Affiliations
- Uppsala University (SE)
- University of Cambridge (GB)
- Corresponding author
- Steffek, Felix
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2024
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4392949855
- Cited by
- 8
- References
- 4
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Law › Legal Language and Interpretation
- Keywords
- legal corpus, court cases, legal AI, GPT-3, GPT-4, RoBERTa, legal documents, UK case law
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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