# RAG-based Architectures for Drug Side Effect Retrieval in LLMs

arXiv: https://arxiv.org/abs/2507.13822 · HTML: https://mlsanity.com/paper/2507.13822/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report](https://mlsanity.com/paper/2402.01733/) (2024)
- [Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study](https://mlsanity.com/paper/2402.15663/) (2024)

## Abstract (arXiv)

Drug side effects are a major global health concern, necessitating advanced methods for their accurate detection and analysis. While Large Language Models (LLMs) offer promising conversational interfaces, their inherent limitations, including reliance on black-box training data, susceptibility to hallucinations, and lack of domain-specific knowledge, hinder their reliability in specialized fields like pharmacovigilance. To address this gap, we propose two architectures: Retrieval-Augmented Generation (RAG) and GraphRAG, which integrate comprehensive drug side effect knowledge into a Llama 3 8B language model. Through extensive evaluations on 19,520 drug side effect associations (covering 976 drugs and 3,851 side effect terms), our results demonstrate that GraphRAG achieves near-perfect accuracy in drug side effect retrieval. This framework offers a highly accurate and scalable solution, signifying a significant advancement in leveraging LLMs for critical pharmacovigilance applications.