Abstract & Overview
Regulatory compliance in the pharmaceutical industry involves navigating complex and voluminous guidelines, often requiring significant amounts of human resources. Recent advancements in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) methods provide promising enhancements to data processing and knowledge management, potentially easing these burdens. However, despite these advancements, conventional Retrieval-Augmented Generation (RAG) methods fall short in this domain due to inherent structural problems. To address these challenges, we introduce the Question and Answer Retrieval Augmented Generation (QA-RAG) framework. This framework enhances the conventional RAG framework. It integrates a dual-track retrieval mechanism tailored to the specific and dynamic nature of pharmaceutical regulations. It utilizes not only the original query but also the answers generated by a fine-tuned LLM, thus providing a more robust foundation for document retrieval. Our experiments demonstrate that QA-RAG outperforms conventional methods in various evaluation metrics including precision, recall, and F1-score. These results underscore QA-RAG's capability to enhance both the accuracy and efficiency of regulatory compliance processes in the pharmaceutical industry. This paper details the structure and efficacy of QA-RAG, emphasizing its potential to revolutionize the regulatory compliance process in the pharmaceutical industry and beyond.