Multi-Legal-Bench: Evaluating LLMs on Legal Reasoning Across Jurisdictions, Languages, and Legal Traditions
arXiv:2605.29738v1 Announce Type: cross Abstract: Legal NLP benchmarks overwhelmingly evaluate a single language or aggregate tasks that differ fundamentally across jurisdictions, making cross-lingual comparison impossible. We introduce Multi-Legal-Bench, the first cross-jurisdictional legal benchmark that evaluates identical tasks across six countries (Ukraine, France, Netherlands, Poland, Czech Republic, Lithuania), four language families, and 134 million court decisions. The benchmark defines five tasks court-type classification, judgment form classification, case-outcome prediction, legal
The proliferation of Large Language Models (LLMs) has necessitated more robust evaluation methods, and legal applications, due to their high-stakes nature, are a critical domain for such advancements.
This benchmark addresses a significant gap in cross-jurisdictional legal AI evaluation, crucial for developing deployable, unbiased, and compliant legal AI systems globally.
The ability to compare and evaluate LLMs on identical legal reasoning tasks across diverse legal traditions and languages will accelerate the development and adoption of AI in legal tech beyond single-jurisdiction applications.
- · Legal AI developers
- · International law firms
- · Governments seeking AI regulation
- · Multi-national corporations
- · AI models without explainability
- · Law firms relying solely on manual research
- · Jurisdictions with limited digital legal data
Improved legal AI models capable of operating effectively across multiple national legal systems.
Increased cross-border legal services automation and enhanced international legal harmonization efforts.
Potential for an 'AI-powered' international legal framework that offers more consistent and efficient dispute resolution.
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Read at arXiv cs.AI