Year: 2026
Type: conference-paper
Abstract: Organizations must continuously monitor evolving regulations to maintain compliance.While current tools are limited to surface-level text comparison, existing models lack the finegrained classification schemes to determine whether small changes impact legal obligations or merely update formatting.To address this gap, we introduce a novel benchmark for change detection in EU regulations.It comprises 4,772 manually annotated pairs of structurally distinct provisions, defined as Atomic Legal Units (ALUs), mapped to a six-class taxonomy of legal change types.We formalize three core tasks: structural alignment, change classification, and a combined task requiring simultaneous alignment and classification.Evaluating lexical algorithms, dense encoders, and Large Language Models (LLMs) as baselines, we find LLMs excel at isolated change classification, whereas domain-specific dense encoders offer the most robust combined performance.By providing fine-grained labeled data, this benchmark enables the development of AI systems that can help organizations analyze regulatory shifts and support version-aware retrieval in the legal domain.
Authors: Joe Yu, Kevin Chenhao Li, Julian Ostarek
Institution: Technical University of Munich
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166832146