用本地化数据微调对话模型，让农村医疗问答无需联网也能用。
Fine-Tuning DialoGPT on Common Diseases in Rural Nepal for Medical Conversations
- 在尼泊尔农村常见病数据上微调DialoGPT，实现离线运行。
- 模型生成回复准确且有同理心，覆盖10种常见病症状描述。
- 适合低资源地区医疗助手开发，无需依赖云端服务。
对话代理正被探索用于支持医疗交付，尤其在资源匮乏的地区如尼泊尔农村。大规模对话模型通常依赖互联网连接和云基础设施，这在农村地区可能不可行。本研究在合成构建的医生-患者交互数据集上微调了轻量级生成对话模型DialoGPT，该数据集涵盖尼泊尔农村常见的十种疾病：普通感冒、季节性发热、腹泻、伤寒、胃炎、食物中毒、疟疾、登革热、结核病和肺炎。尽管训练数据有限且领域特定，微调后的模型仍能生成连贯、上下文相关且医学上恰当的回应，展现出对症状、疾病背景及共情交流的理解。结果表明，紧凑的离线对话模型具有高度适应性，针对性数据集在低资源医疗环境中进行领域适配时非常有效，为未来农村医疗对话AI提供了有前景的方向。
原文摘要 · Abstract (English)
Conversational agents are increasingly being explored to support healthcare delivery, particularly in resource-constrained settings such as rural Nepal. Large-scale conversational models typically rely on internet connectivity and cloud infrastructure, which may not be accessible in rural areas. In this study, we fine-tuned DialoGPT, a lightweight generative dialogue model that can operate offline, on a synthetically constructed dataset of doctor-patient interactions covering ten common diseases prevalent in rural Nepal, including common cold, seasonal fever, diarrhea, typhoid fever, gastritis, food poisoning, malaria, dengue fever, tuberculosis, and pneumonia. Despite being trained on a limited, domain-specific dataset, the fine-tuned model produced coherent, contextually relevant, and medically appropriate responses, demonstrating an understanding of symptoms, disease context, and empathetic communication. These results highlight the adaptability of compact, offline-capable dialogue models and the effectiveness of targeted datasets for domain adaptation in low-resource healthcare environments, offering promising directions for future rural medical conversational AI.
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