---
title: "One Law, Many Languages — paper by Stern et al. (2023)"
description: "The authors also release two in-domain pretraining corpora and three Swiss legal models, and they… 138 references, cited by 22 papers here."
authors: ["Ronja Stern", "Vishvaksenan Rasiah", "Veton Matoshi", "Srinanda Brugger Bose", "Matthias Sturmer", "Ilias Chalkidis", "Daniel E. Ho", "Joel Niklaus"]
year: 2023
venue: "arXiv:2306.09237"
source: "https://arxiv.org/abs/2306.09237"
doi: "10.48550/arxiv.2306.09237"
arxiv: "2306.09237"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/stern-2023-one-law-many-languages-benchmarking/"
---
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# One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support

**Authors:** Ronja Stern, Vishvaksenan Rasiah, [Veton Matoshi](https://md.research.elamur.ai/free/en/authors/veton-matoshi/), Srinanda Brugger Bose, [Matthias Sturmer](https://md.research.elamur.ai/free/en/authors/matthias-sturmer/), [Ilias Chalkidis](https://md.research.elamur.ai/free/en/authors/ilias-chalkidis/), [Daniel E. Ho](https://md.research.elamur.ai/free/en/authors/daniel-e-ho/) and [Joel Niklaus](https://md.research.elamur.ai/free/en/authors/joel-niklaus/) · **Year:** 2023 · **Venue:** arXiv:2306.09237 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2306.09237) · [PDF](https://arxiv.org/pdf/2306.09237) · [DOI 10.48550/arxiv.2306.09237](https://doi.org/10.48550/arxiv.2306.09237) · [arXiv 2306.09237](https://arxiv.org/abs/2306.09237) · [OpenAlex](https://openalex.org/W6907707068)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [22](https://md.research.elamur.ai/free/en/papers/stern-2023-one-law-many-languages-benchmarking/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Strong LLMs have saturated many general NLP benchmarks, but legal and multilingual evaluation suites are still scarce, and most public models are trained mostly on English text. Stern et al. address this gap with a benchmark built from Swiss court decisions and legislation. Switzerland has one jurisdiction, several official languages and much published case law, so it suits this kind of evaluation. The authors created seven new datasets from the Swiss Federal Supreme Court and 26 cantonal courts. The tasks cover classification, document-to-document retrieval, Citation Extraction, generating court reasoning and summarizing leading decisions, and some inputs reach 50K tokens. The authors also release two in-domain pretraining corpora and three Swiss legal models, and they compare these with multilingual baselines and ChatGPT. Domain pretraining improves scores, but the tasks remain hard: the strongest system reaches only about 48 aggregated Macro F1. The benchmark gives the community a demanding, openly licensed testbed for long-document legal reasoning in languages other than English.

## Key points
- The benchmark covers German, French, Italian and Romansh as well as English, for a total of five languages. Some datasets contain hundreds of thousands of examples.
- The tasks are:
  - Criticality Prediction, Judgment Prediction and Law Area Prediction, which together make up eight Text Classification tasks
  - Information Retrieval and Citation Extraction
  - Court View Generation and Leading Decision Summarization
- The new models are Legal-Swiss-RoBERTa Base/Large and Legal-Swiss-Longformer Base. They were pretrained on the released Swiss legal corpora.
- The best model scores 48.4 aggregated Macro F1. ChatGPT trails fine-tuned models on the classification tasks, and results for CP, IR and CVG look close to arbitrary.
- All datasets, models and code are published under CC BY-SA, with the data hosted on the HuggingFace Hub.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Fribourg](https://md.research.elamur.ai/free/en/universities/university-of-fribourg/) — University, Fribourg, Switzerland · 4 works · 200 citations
- [University of Bern](https://md.research.elamur.ai/free/en/universities/university-of-bern/) — University, Bern, Switzerland · 5 works · 605 citations
- [Bern University of Applied Sciences](https://md.research.elamur.ai/free/en/universities/bern-university-of-applied-sciences/) — University, Bern, Switzerland · 4 works · 154 citations

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## Cite

Cite as: Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi, Srinanda Brugger Bose, Matthias Sturmer, Ilias Chalkidis, Daniel E. Ho and Joel Niklaus. 2023. One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support. arXiv:2306.09237.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{stern2023one,
  title = {{One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support}},
  author = {Ronja Stern, Vishvaksenan Rasiah, Veton Matoshi, Srinanda Brugger Bose, Matthias Sturmer, Ilias Chalkidis, Daniel E. Ho and Joel Niklaus},
  year = {2023},
  doi = {10.48550/arxiv.2306.09237},
  eprint = {2306.09237},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2306.09237}
}
```

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*Updated 30 October 2026*