用微调大模型提升医疗问答准确率，让医生快速获取可信信息。
Fine-Tuning LLMs for Reliable Medical Question-Answering Services
- 采用rsDoRA+与ReRAG技术优化LLaMA-2和Mistral模型性能
- 在医疗问答任务中实现更高准确率与响应可靠性
- 适合临床辅助决策系统开发人员参考使用
本文提出一种先进的医疗问答服务方法，通过微调大型语言模型（LLMs）提升医疗信息的准确性与可靠性。研究聚焦于优化LLaMA-2和Mistral等模型，利用综合数据集，结合rsDoRA+与ReRAG技术。rsDoRA+通过分解模型权重、对低秩矩阵采用不同学习率并稳定秩数，提升效率；ReRAG则融合按需检索与问题重写，进一步提高回答精度。该方法使医疗提供者能快速获取可靠信息，支持更高效决策，增强患者信任。结果表明，微调后的LLMs显著提升医疗信息服务质量与可及性，有助于改善整体医疗效果。
原文摘要 · Abstract (English)
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.
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