Clash-of-Leges: A bilingual dataset for conflict detection and explanation in statutory law
Year: 2025
Type: article
Abstract: • Comprehensive Legal Conflict Dataset: Clash-of-Leges introduces a novel dataset that captures authentic legal conflicts between provisions across different documents, derived from Italian Constitutional Court rulings, to support advanced AI research in legal conflict detection and explanation. • Bilingual and Multi-Task Framework: The dataset includes annotations for three key tasks-Conflict Detection, Conflict Explanation Generation, and Reference Retrieval-while offering bilingual support in Italian and English to enhance global research applicability. • Extensive Evalutions: The manuscript resents an extensive experimental evaluation of several large language models across all defined tasks, illustrating both their capabilities and current limitations. Legal conflicts between statutes or constitutional articles present a significant challenge in maintaining consistency and coherence within legal systems. Addressing these conflicts requires extensive human expertise, making the process labor-intensive and time-consuming. In this paper, we introduce Clash-of-Leges , a novel multilingual dataset derived from rulings by the Constitutional Court of the Italian Republic, designed to aid the automation of conflict detection and explanation between legal articles. We identify three key tasks: Conflict Classification, which determines whether two legal articles are in conflict; Conflict Explanation Generation, which provides detailed explanations for identified conflicts; and Reference Retrieval, which sources relevant legal bases and precedents to substantiate interpretations. These tasks are intended to facilitate the development of AI models that can automatically identify and explain contradictions between legal provisions. 2
Source: Expert Systems with Applications
Authors: Paolo Italiani, Gianluca Moro, Luca Ragazzi
Institution: University of Bologna
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
Award: G8145344908
OpenAlex ID: W4416058867
