A Novel Approach Based on Integrating Small Language Models and Retrieval-Augmented Generation for Medical Question Answering
TL;DR
A novel approach which combines small language models with retrieval-augmented generation in medical question answering to provide accurate and comprehensible information, and supports efficient domain knowledge updates without the need for extensive re-training.
Abstract
—This paper proposes a novel approach which combines small language models with retrieval-augmented generation in medical question answering to provide accurate and comprehensible information. Our method extracts relevant evidence from external knowledge and converts them into vector embeddings which are used for high-fidelity semantic retrieval. The small language model subsequently synthesizes the retrieved evidence into fluent, context-aware responses. Extensive experiments conducted on the PubMed benchmark dataset, the results show competitive performance to larger language models while being far more suitable for deployment in resource-constrained environments. Moreover, the proposed method supports efficient domain knowledge updates without the need for extensive re-training. Our implementation is available in the following GitHub repository: https://github.com/LeoBaoNguyen12/RagSLM-MQA.