mlsanityData to 7 Oct 2026

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

arXiv 2402.01717 ↗First posted 26 Jan 2024cs.CL cs.AI cs.IRcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 30 of its 47 references that are in the map. First posted on arXiv 26 Jan 2024.

Builds on

30 of 47 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Ragas: Automated Evaluation of Retrieval Augmented Generation26 Sep

Other references · 29

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
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  11. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  12. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 414
17 more
  1. 2020Document Ranking with a Pretrained Sequence-to-Sequence Modelcited by 373
  2. 2023Nougat: Neural Optical Understanding for Academic Documentscited by 240
  3. 2019Multi-Stage Document Ranking with BERTcited by 200
  4. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  5. 2023Query2doc: Query Expansion with Large Language Modelscited by 146
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  11. 2021More Robust Dense Retrieval with Contrastive Dual Learningcited by 15
  12. 2023GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidencecited by 14
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  17. 2019An Empirical Comparison of FAISS and FENSHSES for Nearest Neighbor Search in Hamming Spacecited by 1

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