用知识图谱让英语大模型跨语言答医问题，准确率最高提升35%
MKG-Rank: Enhancing Large Language Models with Knowledge Graph for Multilingual Medical Question Answering
- 构建多语言知识图谱，通过词级翻译融合英文医学知识到大模型推理
- 跨语言问答准确率提升最高达35.03%，平均检索仅需0.0009秒
- 适合低资源语种医疗问答场景，尤其对中文、日文、韩文和斯瓦希里文有效
大型语言模型（LLMs）在医学问答中表现卓越，但其能力主要局限于英语，受限于多语言训练数据不平衡及低资源语言医疗资源匮乏。为解决这一关键语言差距，我们提出多语言知识图谱增强型检索排序框架MKG-Rank，使以英语为中心的LLM可执行多语言医学问答。通过词级翻译机制，该框架以低成本高效整合全面的英语医学知识图谱，缓解跨语言语义失真，实现跨语言精准医学问答。为提升效率，引入缓存与多角度排序策略优化检索过程，显著降低响应时间并优先筛选相关医学知识。在中文、日文、韩文和斯瓦希里文等多语言医学问答基准上的大量评估表明，MKG-Rank持续优于零样本LLM，准确率最高提升35.03%，且平均检索时间仅为0.0009秒。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced multilingual training data and scarce medical resources for low-resource languages. To address this critical language gap in medical QA, we propose Multilingual Knowledge Graph-based Retrieval Ranking (MKG-Rank), a knowledge graph-enhanced framework that enables English-centric LLMs to perform multilingual medical QA. Through a word-level translation mechanism, our framework efficiently integrates comprehensive English-centric medical knowledge graphs into LLM reasoning at a low cost, mitigating cross-lingual semantic distortion and achieving precise medical QA across language barriers. To enhance efficiency, we introduce caching and multi-angle ranking strategies to optimize the retrieval process, significantly reducing response times and prioritizing relevant medical knowledge. Extensive evaluations on multilingual medical QA benchmarks across Chinese, Japanese, Korean, and Swahili demonstrate that MKG-Rank consistently outperforms zero-shot LLMs, achieving maximum 35.03% increase in accuracy, while maintaining an average retrieval time of only 0.0009 seconds.
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