A Novel RAG Framework with Knowledge-Enhancement for Biomedical Question Answering

The biomedical question-answering system usually provide accurate and real-time responses, which is crucial for clinical decision-making and scientific research. Although large language models achieve remarkable results in general question-answering tasks, they still face challenges in specialized fields. This paper proposes a novel framework called RAG-Chain, which aims to enhance the performance of general-domain large models on special biomedical reasoning and question-answering tasks. The RAG-Chain framework improves the knowledge retrieval and generation abilities of general models by a multi-stage processing of external knowledge and automatic construction of chain-of-thought templates combined with self-consistency validation process of choice shuffling. The experimental results show that RAG-Chain improves the accuracy of the baseline model by an average of 6.9% on the MedQA dataset without the need for pre-training or fine-tuning in biomedical fields, verifying its strong adaptability and effectiveness in different large language models.