Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework

This article presents a comparative study on the performance of fine-tuned and zero-shot large language models (LLMs) within a Retrieval-Augmented Generation (RAG) framework for medical question-answering. The research likely aims to identify the most effective approach for improving the accuracy and reliability of medical information retrieval and response generation. The use of RAG suggests an attempt to mitigate the limitations of LLMs by incorporating external knowledge sources.