传染病的阿拉伯大语言模型InfectA-Chat:比较分析
Yesim Selcuk1, Eunhui Kim1,2, Insung Ahn1,3
1Department of Applied AI, KISTI School, University of Science and Technology, Daejeon, Republic of Korea.
JMIR medical informatics
|February 10, 2025
概括
一个新的双语AI工具InfectA-Chat,提供了最新的阿拉伯语和英语传染病信息. 该系统克服了语言障碍,改善了全球公共卫生监测和个人对关键健康数据的访问.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 公共卫生信息学 公共卫生信息学
背景情况:
- 传染病是全球公共卫生面临的重大挑战,需要强大的监测系统.
- 现有的监控平台主要使用英语,为非英语使用者创造了可访问性障碍,特别是在中东等地区.
- 中东呼吸综合征冠状病毒 (MERS-CoV) 爆发突显了需要本地化,可访问的疾病监测工具的需求.
研究的目的:
- 推出InfectA-Chat,这是一种新型的大型语言模型 (LLM),用于双语 (阿拉伯语/英语) 传染病问题和答案任务.
- 通过克服疾病监测中的语言障碍,增强全球公共卫生工作和个人理解.
主要方法:
- 使用55,400条阿拉伯语和英语域特定数据集对AceGPT-7B和AceGPT-7B-Chat模型进行指令调整.
- 通过GPT-4评估对2,770个特定领域的指令遵循数据点进行性能评估.
- 与领先的阿拉伯LLM和最先进的模型进行比较分析 (例如,Jais-13B-Chat,Gemini,GPT-4).
- 整合取回增强生成 (RAG) 以实时更新数据而无需重新培训.
主要成果:
- 在传染病问答方面,InfectA-Chat表现出强的表现,通过GPT-4验证.
- 该模型的表现优于现有的阿拉伯LLM,包括AceGPT-7B-Chat (43.52%) 和Jais-13B-Chat (48.61%).
- 与最先进的模型相比,InfectA-Chat取得了具有竞争力的结果,与GPT-4相比显示了23.78%的领先性能.
- 该RAG方法显著提高了文档检索准确性,在更高的top-k参数值下得到更好的结果.
结论:
- 一般阿拉伯语LLM在提供当前传染病信息方面存在局限性.
- InfectA-Chat提供了一种有价值的双语解决方案,以赋予个人和公共卫生倡议在传染病监测方面的权力.
- 该研究强调了开发专门的AI工具的重要性,以解决特定的公共卫生信息需求和语言差异.
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