We present an advanced approach to medical question-answering (QA) services, using fine-tuned Large Language Models (LLMs) to improve the accuracy and reliability of healthcare information. Our study focuses on optimizing models like LLaMA-2 and Mistral, which have shown great promise in delivering precise, reliable medical answers. By leveraging comprehensive datasets, we applied fine-tuning techniques such as rsDoRA+ and ReRAG. rsDoRA+ enhances model performance through a combination of decomposed model weights, varied learning rates for low-rank matrices, and rank stabilization, leading to improved efficiency. ReRAG, which integrates retrieval on demand and question rewriting, further refines the accuracy of the responses. This approach enables healthcare providers to access fast, dependable information, aiding in more efficient decision-making and fostering greater patient trust. Our work highlights the potential of fine-tuned LLMs to significantly improve the quality and accessibility of medical information services, ultimately contributing to better healthcare outcomes for all.

本研究针对医疗问答服务中的准确性和可靠性问题，提出了一种使用微调的大型语言模型（LLMs）的先进方法。我们通过优化LLaMA-2和Mistral模型，结合rsDoRA+和ReRAG等微调技术，显著提高了医疗信息的精确度和可获得性，从而改善了医疗服务的质量和患者的信任度。

为可靠的医疗问答服务微调大型语言模型