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        <title>RegTrack: A Fine-Grained Benchmark for Multi-Class Legal Change Detection</title>
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            <title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)</title>
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            <namePart type="given">Santosh</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T RegTrack: A Fine-Grained Benchmark for Multi-Class Legal Change Detection
%A Yu, Joe
%A Li, Kevin Chenhao
%A Ostarek, Julian
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F yu-etal-2026-regtrack
%X 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.
%U https://aclanthology.org/2026.acl-srw.68/
%P 764-778

Markdown (Informal)

[RegTrack: A Fine-Grained Benchmark for Multi-Class Legal Change Detection](https://aclanthology.org/2026.acl-srw.68/) (Yu et al., ACL 2026)

• RegTrack: A Fine-Grained Benchmark for Multi-Class Legal Change Detection (Yu et al., ACL 2026)

ACL

• Joe Yu, Kevin Chenhao Li, and Julian Ostarek. 2026. RegTrack: A Fine-Grained Benchmark for Multi-Class Legal Change Detection. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 764–778, San Diego, California, United States. Association for Computational Linguistics.