构建首个印地语系多轮医疗对话数据集，助力本地化精准问诊。
IndicMedDialog: A Parallel Multi-Turn Medical Dialogue Dataset for Accessible Healthcare in Indic Languages
- 用大模型生成+人工校对构建跨语言多轮医患对话数据
- 在10种语言上验证模型表现，临床合理性获专家认可
- 适配小模型量化部署，支持个性化症状追问
现有医疗对话系统多为单轮问答或依赖模板数据，限制了对话真实性和多语言适用性。我们提出IndicMedDialog，一个涵盖英语及九种印地语系语言（阿萨姆语、孟加拉语、古吉拉特语、印地语、马拉地语、旁遮普语、泰米尔语、泰卢固语、乌尔都语）的平行多轮医疗对话数据集。该数据集在MDDial基础上，通过大模型生成合成问诊内容，使用TranslateGemma进行翻译，并经母语者验证，再通过基于脚本的后处理流程修正音素、词汇和字符间距错误。基于此数据集，我们采用参数高效微调方法，对量化的小型语言模型IndicMedLM进行训练，可选患者先验上下文以实现多轮症状采集的个性化。我们在十种语言上评估模型表现，对比零样本多语言基线，并通过医学专家评估验证临床合理性。
原文摘要 · Abstract (English)
Most existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational realism and multilingual applicability. We introduce IndicMedDialog, a parallel multi-turn medical dialogue dataset spanning English and nine Indic languages: Assamese, Bengali, Gujarati, Hindi, Marathi, Punjabi, Tamil, Telugu, and Urdu. The dataset extends MDDial with LLM-generated synthetic consultations, translated using TranslateGemma, verified by native speakers, and refined through a script-aware post-processing pipeline to correct phonetic, lexical, and character-spacing errors. Building on this dataset, we fine-tune IndicMedLM via parameter-efficient adaptation of a quantized small language model, incorporating optional patient pre-context to personalise multi-turn symptom elicitation. We evaluate against zero-shot multilingual baselines, conduct systematic error analysis across ten languages, and validate clinical plausibility through medical expert evaluation.
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