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

Jaewoong Kim, Moohong Min
1/26/2024
cs.CLcs.AIcs.IR

No official code found. Best implementation: jwoongkim11/QA-RAG (24 stars, MIT, last commit 2024-09-09).

Abstract

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.

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Code Implementations(1)

jwoongkim11/QA-RAGCommunity100%
MIT

Code for the paper, From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process

247Python, Jupyter NotebookJan 15, 20242 years agoMIT

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Cite this paper

@article{kim2024from,
  title  = {From RAG to QA-RAG: Integrating Generative AI for Pharmaceutical Regulatory Compliance Process},
  author = {Jaewoong Kim and Moohong Min},
  year   = {2024},
  eprint = {2402.01717},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.01717v1}
}

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