一套用于公共卫生信息监控的大型语言模型
Xinyu Zhou1,2, Jiaqi Zhou1,2, Chiyu Wang3
1Division of Biostatistics and Informatics, Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA.
NPJ digital medicine
|February 23, 2026
概括
我们开发了PH-LLM,这是一个新的大型语言模型 (LLM) 套件,用于实时公共卫生信息监控. PH-LLM 展示了卓越的多语言能力,增强了公共卫生监测和响应工作.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 社交媒体对于公共卫生参与至关重要.
- 目前的信息监控方法缺乏实时能力,阻碍了及时的公共卫生反应.
- 需要先进的工具来监控在线公共卫生讨论.
研究的目的:
- 开发和评估PH-LLM,这是一套用于实时公共卫生信息监控的大型语言模型 (LLM) 的新套件.
- 评估PH-LLM在英语和多语言任务方面的表现.
- 为监测公众健康情绪提供一个具有成本效益的解决方案.
主要方法:
- 开发了PH-LLM,一套LLM,使用QLoRA和LoRA加技术与Qwen 2.5.5一起开发.
- 策划了PH-LLM的多语言培训集体.
- 构建了19个英语和20个多语言持有任务的基准,用于零射击评估.
主要成果:
- 在英语和多语言任务中,PH-LLM始终优于基准LLM.
- PH-LLM-14B和PH-LLM-32B超越了GPT-4o和Llama-3.1-70B-Instruct等领先的模型.
- 在英语任务中达到>=56.0%,在多语言任务中达到>=59.6%,超过了基线.
结论:
- PH-LLM为实时公共卫生信息监控提供了最先进的性能.
- 该模型为全球健康监测提供了先进的多语言功能.
- PH-LLM在网上了解公共卫生情绪方面提供了具有成本效益的进步.
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