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arXiv · 2605.29738

Multi-Legal-Bench: Evaluating LLMs on Legal Reasoning Across Jurisdictions, Languages, and Legal Traditions

Volodymyr Ovcharov
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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 165 million full-text court decisions. The benchmark defines five tasks (court-type classification, judgment form classification, case-outcome prediction, legal norm extraction, and cause category prediction) mapped to structured metadata from national court registries, forming a deliberately sparse 5x6 task-jurisdiction matrix (20 of 30 cells filled). We evaluate 7 frontier LLMs under zero-shot and 3-shot prompting via AWS Bedrock, with 4 additional small/medium models (3-12B) for scaling analysis. Our results reveal that: (1) few-shot gains are uneven and track how much headroom a cell leaves rather than its language, with 8 of 28 judgment-form model-jurisdiction pairs losing accuracy; (2) no single model dominates any language, rankings shift with both task and jurisdiction; (3) cross-lingual few-shot transfer does not follow language proximity: UA->FR (Romance, -2.0 pp) transfers better than UA->PL (Slavic, -13.8 pp), with label-set alignment predicting transfer quality better than language family; and (4) tokenizer fertility, despite a 2.3x spread, does not significantly predict cross-lingual accuracy (r=-0.14, p=0.24), suggesting that model architecture and pretraining data dominate tokenizer efficiency. We release all data, prompts, and model predictions.

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Volodymyr Ovcharov. 2026-08-07. Multi-Legal-Bench: Evaluating LLMs on Legal Reasoning Across Jurisdictions, Languages, and Legal Traditions. https://arxiv.org/abs/2605.29738

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