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Τεκμήριο Retrieval augmented generation on regulatory documents(2025-06-20) Chasandras, Ioannis; Χασάνδρας, Ιωάννης; Androutsopoulos, Ion; Chlapanis, OdysseasThis thesis investigates the application of Retrieval Augmented Generation (RAG) in regulatory procedures through the emerging field of Regulatory NLP. Based on real-world regulatory documents, the study evaluates the performance of commercial retrieval models and introduces advanced, hybrid retrieval techniques tailored for legal compliance tasks. Given the critical need for precision and completeness in the legal domain, new algorithms that utilize Large Language Models (LLMs) are developed to enhance regulatory question-answering. The work also includes an adversarial evaluation of RePASs, a metric focused on legal obligations. Through participation in the RIRAG-2025 shared task, the thesis demonstrates both the promise and current limitations of AI systems in regulatory settings, emphasizing the need for further exploration in this field.