This work introduces RegNLI, a novel framework that formulates misbranding detection as a inference task between product claims and regulatory provisions, and builds a foundation for compliance-aware NLP systems and opens new directions for integrating formal reasoning with neural architectures in regulatory domains.
This paper analyzes metadata from Ecuador's SOCE, with particular emphasis on participant comments generated during the pre-contractual phase to propose a hybrid modeling framework that integrates unsupervised clustering and supervised classification within a natural language processing (NLP) pipeline to uncover latent patterns and detect potentially irregular procurement processes.
Bryan Torres, Daniel Riofrío, J. Vega-Sanchez et al.· IEEE Transactions on Computa...· 0 citations
This study presents a novel, manually annotated dataset of 121 business news articles related to five major steel companies, using the Cambridge Risk Taxonomy, and provides empirical evidence on the opportunities and limitations of LLMs for both analytical and narrative forms of automated risk assessment.
Laleh Davoodi, Filip Ginter, Sima Salimi et al.· SN Computer Science· 0 citations
A sequential behavioral alignment framework pairing fine-tuning with preference optimization over paired correct and counterfactual rationales is developed and applied, demonstrating that behavioral alignment mitigates bidirectional rationalization while delivering human-interpretable reasoning traces without manual pipeline overhead.
Alireza S. Ziabari, Kat Ellis, Colleen E. Chan et al.· 0 citations
This work presents PROSLEX (PRediction Of Statutes and LEgal eXplanation), a comprehensive dataset comprising 1,623 expert-annotated legal documents from the Indian context, positioning PROSLEX as a benchmark for developing explainable AI systems that can support legal practitioners while advancing research in interpretable legal NLP.
Subinay Adhikary, Upal Bhattacharya, Vivek K. Singh et al.· 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
U N M ASK is presented, a fully automated pipeline that discovers, causally verifies, and mitigates spurious correlations in text classifiers without additional human annotation, and demonstrates that the discovery and validation stages generalize to reward model preference data.
Chidaksh Ravuru, Shashank Srivastava· 0 citations
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