RegNLI: Detecting Online Product Misbranding through Legal and Linguistic Alignment

AbstractMisbranding of health-related products poses significant risks to public safety and regulatory compliance. Existing approaches to claim verification largely rely on keyword matching or generic text classification, failing to capture the nuanced reasoning required to align product claims with legal statutes. In this work, we introduce RegNLI, a novel framework that formulates misbranding detection as a inference task between product claims and regulatory provisions. Leveraging a curated dataset of FDA warning letters, we construct structured representations of claims and statutes. Our model integrates a regulation-aware gating mechanism with a contrastive alignment objective to jointly optimize misbranding classification and statute mapping. Experiments on the FDA-Misbrand dataset demonstrate that RegNLI significantly outperforms strong baselines across accuracy, F1-score, and regulation alignment metrics, while providing interpretable attention patterns that highlight critical linguistic cues. This work establishes a foundation for compliance-aware NLP systems and opens new directions for integrating formal reasoning with neural architectures in regulatory domains.