RegTrack: A Fine-Grained Benchmark for Multi-Class Legal Change Detection
Joe Yu Technical University of Munich, Kevin Chenhao Li Technical University of Munich, Julian Ostarek Shuttle Technologies and Technical University of Munich
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.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop) · 2026 · 正式发表