One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
Keywords: law, legal, reasoning, multilingual, benchmark, datasets
TL;DR: A multilingual benchmark for legal reasoning supporting the justice system
Abstract: Recent strides in Large Language Models (LLMs) have saturated many NLP benchmarks (even professional domain-specific ones), emphasizing the need for more challenging ones to properly assess LLM capabilities. Domain-specific and multilingual benchmarks are rare, since they require high expertise to develop. Still, most public models are trained predominantly on English corpora, while other languages remain understudied, particularly for practical domain-specific NLP tasks. In this work, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing \emph{long documents} (up to 50K tokens), using \emph{domain-specific knowledge} (embodied in legal texts), \emph{multilingual} understanding (covering five languages), and \emph{multitasking} (comprising legal document-to-document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks). Our benchmark contains diverse datasets from the Swiss legal system, allowing for a comprehensive study of the underlying non-English, inherently multilingual, federal legal system. Despite the large size of our datasets (tens to hundreds of thousands of examples), existing publicly available multilingual models struggle with most tasks, even after extensive in-domain pre-training and fine-tuning. We publish all resources (benchmark suite, pre-trained models, code) under fully permissive open CC BY-SA licenses.
Primary Subject Area: Domain specific data issues
Paper Type: Research paper: up to 8 pages
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Submission Number: 20
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