从RAG到QA-RAG：集成生成式AI用于制药行业法规合规流程
From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process
AI总结：
本研究提出QA-RAG模型，结合生成式AI与检索增强生成技术，用于制药法规合规问答，在对比实验中显著提升准确性，优于传统RAG等基线方法。
摘要：
制药行业的法规合规需要应对复杂且庞大的指导文件，通常需要大量人力资源。为应对这些挑战，我们的研究引入了一种利用生成式AI和检索增强生成（RAG）方法的聊天机器人模型。该聊天机器人旨在搜索与用户查询相关的指导文件，并基于检索到的指导文件提供答案。鉴于该领域对高可靠性的内在需求，我们提出了问答检索增强生成（QA-RAG）模型。在对比实验中，QA-RAG模型在准确性上表现出显著提升，优于包括传统RAG方法在内的所有其他基线。本文详细介绍了QA-RAG的结构和性能评估，强调了其在制药行业及其他领域的法规合规方面的潜力。我们已将我们的工作公开，以供进一步研究和开发。
英文摘要：
Regulatory compliance in the pharmaceutical industry entails navigating through complex and voluminous guidelines, often requiring significant human resources. To address these challenges, our study introduces a chatbot model that utilizes generative AI and the Retrieval Augmented Generation (RAG) method. This chatbot is designed to search for guideline documents relevant to the user inquiries and provide answers based on the retrieved guidelines. Recognizing the inherent need for high reliability in this domain, we propose the Question and Answer Retrieval Augmented Generation (QA-RAG) model. In comparative experiments, the QA-RAG model demonstrated a significant improvement in accuracy, outperforming all other baselines including conventional RAG methods. This paper details QA-RAG's structure and performance evaluation, emphasizing its potential for the regulatory compliance domain in the pharmaceutical industry and beyond. We have made our work publicly available for further research and development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
