RAG-based Architectures for Drug Side Effect Retrieval in LLMs
This provides a scalable solution for pharmacovigilance, addressing a critical health concern, but it is incremental as it adapts existing RAG methods to a specific domain.
The paper tackled the problem of unreliable drug side effect retrieval in LLMs by proposing RAG and GraphRAG architectures integrated with Llama 3 8B, achieving near-perfect accuracy on a dataset of 19,520 drug side effect associations.
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.