Comma: A multi-task and multi-lingual dataset of constitutional verdicts

Abstract Transformer-based language models have sparked a revolutionary change in Legal NLP, endowing lawyers with unparalleled tools to effectively navigate, understand, and draft large volumes of text. However, the dearth of large-scale datasets from authoritative sources hampers further progress. The available resources are primarily single-task, English-only, and written in layman’s terms. To bridge this gap, we introduce Comma , a multi-task and multi-lingual archive of 14K verdicts drawn from the Constitutional Court of the Italian Republic, grounded in a non-common law system. Documents in Comma diverge from ordinary legal manuscripts as they address fundamental principles and rights, involve technical jargon, exhibit an articulated structure, are diachronic, have extended length, and demand more significant expertise and interpretation. By embracing 4 widespread languages, Comma tackles a panoply of necessity-driven tasks: multi-granular abstractive summarization, decision generation, article retrieval, and ruling classification. We systematically benchmark a catalog of language models in both few-shot and full settings, uncovering substantial headroom for improvement. We contribute to the new era of Legal NLP systems by openly releasing Comma and best-performing models (https://github.com/disi-unibo-nlp/comma).