2026· Annual Meeting of the Association for Computational Linguistics· pp. 764-778· 0 citations· 45 references
Computer Science
TL;DR
A novel benchmark for change detection in EU regulations is introduced that 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.
Abstract
Organizations must continuously monitor evolving regulations to maintain compliance. While current tools are limited to surface-level text comparison, existing models lack the fine-grained 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.
Tabular Anomaly Detection (TAD) plays a fundamental role in securing real-world applications. Despite rapid advances in TAD, the prohibitive cost of human-centric label annotation remains a primary bottleneck for large-scale production systems. To alleviate this bottleneck, we propose a novel ''coarse-to-fine'' label annotation pipeline to improve labor efficiency through a coarse-grained label annotation and fine-grained human verification. Specifically, Large Language Models (LLMs), with their strong cross-domain capabilities, serve as a promising solution for the coarse-grained annotation stage. However, effectively generalizing LLMs to coarse-grained annotation remains challenging due to the inability to ground semantic priors in rigorous deduction, as well as the overfitting risks inherent in single-domain fine-tuning. Accordingly, we introduce TaDGeneral, a large-scale cross-domain corpus constructed by fusing deductive reasoning paths from diverse domains. This design bridges the reasoning gap while preventing the memorization of local shortcuts. Building upon this, we develop TaDFM, a foundation model tailored to internalize generalizable deductive logic for effective zero-shot annotation. Extensive experiments on both public and large-scale real-world TAD datasets demonstrate the superiority of TaDFM over representative methods, with its practical value further validated by an industrial case study. Code: https://github.com/cshhzhao/TaDFM.
Haihong Zhao, Aochuan Chen, Miao Peng et al.· Proceedings of the 32nd ACM...· 0 citations
A survey and comparative analysis of NLP-based Automatic Deception Detection focusing on the legal domain and the evolution from feature-based machine learning to Large Language Model (LLM) approaches are presented, showing strong domain sensitivity.
T. Samaradiwakara, Nisansa de Silva, George C. Lobb· arXiv.org· 0 citations
Findings identify evidence localization and fine-grained type discrimination as distinct challenges and show that compact supervised encoders are strong baselines for this task.
Aman Kumar, Lasitha Vidyaratne, Dipanjan Ghosh et al.· arXiv.org· 0 citations
ExReg, a human-in-the-loop workflow that automatically generates discriminative examples using SMT-based constraint solving, highlights how automated example generation guided by formal methods and mutations can improve the reliability, efficiency, and trustworthiness of LLM-assisted regex pattern generation.
Hadar Muchtar, Ilia Shevrin, S. Maoz· ACM Transactions on Software...· 0 citations
This paper introduces the task of identifying and segmenting legal conditions (Tatbestand) and legal consequences (Rechtsfolge) within German statutory texts and presents ANNOTARES (Annotations of Tatbestand-Rechtsfolge Sequences), a novel dataset comprising German law texts with span-level annotations.
Tasks such as customs tariff classification, export control categorization, and standards-based equipment coding require assigning an input instance to a fine-grained class under an explicit regulatory hierarchy. Unlike standard text classification, the correct label in these tasks is not determined by semantic similarity alone, but by rule-defined boundaries, threshold conditions, exclusion clauses, definitions, and local exceptions. As a result, two highly similar inputs may require different labels, while a retrieved passage that appears relevant may still be inapplicable under the governing rules. Existing flat classifiers, hierarchical text classification methods, and retrieval-augmented LLM systems are not designed to jointly enforce hierarchical validity, rule consistency, and fine-grained boundary reasoning. In this paper, we formulate this setting as regulation-driven fine-grained hierarchical classification, where an external instance must be assigned to a fine-grained class through a valid path in a regulatory hierarchy and supported by auditable evidence. We construct four benchmark datasets from representative regulation-intensive scenarios and validate the annotations through an expert-in-the-loop process. We further propose a constraint-aware hierarchical search framework that converts regulatory documents into a searchable tree, retrieves only valid local candidate nodes, and uses structured regulatory fields with evidence snippets to guide each next-hop decision. Experiments show that our method achieves the best mean accuracy on all four datasets and provides interpretable decision paths, with the largest gains on cases involving fine-grained neighboring categories and rule-based boundary conditions.
Siyu Wang, Weiwu Tan, Lu-Lu Chen· 0 citations
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