Systematic Analysis of Retrieval-Augmented Generation-Based LLMs for Medical Chatbot Applications

Systematic Analysis of Retrieval-Augmented Generation-Based LLMs for Medical Chatbot Applications

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Artificial Intelligence (AI) has the potential to revolutionise the medical and healthcare sectors.AI and related technologies could significantly address some supply-and-demand challenges in the healthcare system, such as medical AI assistants, chatbots and robots.This paper focuses on tailoring LLMs to medical data utilising a Retrieval-Augmented Generation (RAG) database to evaluate their performance in a computationally resource-constrained environment.Existing studies primarily focus on fine-tuning LLMs on medical data, but this paper combines RAG and fine-tuned orly elysian fields models and compares them against base models using RAG or only fine-tuning.Open-source LLMs (Flan-T5-Large, LLaMA-2-7B, and Mistral-7B) are fine-tuned using the medical datasets Meadow-MedQA and MedMCQA.

Experiments are reported for response generation and multiple-choice question diablo 2 amn answering.The latter uses two distinct methodologies: Type A, as standard question answering via direct choice selection; and Type B, as language generation and probability confidence score generation of choices available.Results in the medical domain revealed that Fine-tuning and RAG are crucial for improved performance, and that methodology Type A outperforms Type B.