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.

该研究介绍了一种利用生成型 AI 和检索增强生成（RAG）方法的聊天机器人模型，该模型旨在搜索与用户查询相关的指南文件并基于检索到的指南提供答案。通过比较实验，QA-RAG 模型在准确性方面表现出显著提升，并超越了所有其他基线方法，包括传统的 RAG 方法。本文详细介绍了 QA-RAG 的结构和性能评估，并强调其在药品监管合规领域和其他领域的潜在应用。我们已将我们的工作公开提供以供进一步的研究和开发。

从 RAG 到 QA-RAG：整合生成型人工智能以支持药物监管合规流程

From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical  Regulatory Compliance Process

University students often spend a considerable amount of time seeking answers
to common questions from administrators or teachers. This can become tedious
for both parties, leading to a need for a solution. In response, this paper
proposes a chatbot model that utilizes natural language processing and deep
learning techniques to answer frequently asked questions (FAQs) in the Amharic
language. Chatbots are computer programs that simulate human conversation
through the use of artificial intelligence (AI), acting as a virtual assistant
to handle questions and other tasks. The proposed chatbot program employs
tokenization, normalization, stop word removal, and stemming to analyze and
categorize Amharic input sentences. Three machine learning model algorithms
were used to classify tokens and retrieve appropriate responses: Support Vector
Machine (SVM), Multinomial Na\"ive Bayes, and deep neural networks implemented
through TensorFlow, Keras, and NLTK. The deep learning model achieved the best
results with 91.55% accuracy and a validation loss of 0.3548 using an Adam
optimizer and SoftMax activation function. The chatbot model was integrated
with Facebook Messenger and deployed on a Heroku server for 24-hour
accessibility. The experimental results demonstrate that the chatbot framework
achieved its objectives and effectively addressed challenges such as Amharic
Fidel variation, morphological variation, and lexical gaps. Future research
could explore the integration of Amharic WordNet to narrow the lexical gap and
support more complex questions.

提出了一种利用自然语言处理和深度学习技术来回答阿姆哈拉语常见问题的聊天机器人模型，并通过 Facebook Messenger 和 Heroku 服务器进行 24 小时访问，实验证明该聊天机器人框架有效应对了阿姆哈拉语各种挑战。