使用LLM和知识蒸有效地提取信息:关于HPV健康传播的研究
Saadat Hasan Khan1, Kevin Lybarger2
1Department of Computer Science, George Mason University, Fairfax, Virginia, United States of America.
PLOS digital health
|March 10, 2026
概括
一个新的框架有效地评估州卫生部 (DOH) 网站的人类乳头瘤病毒 (HPV) 疫苗信息质量. 一个微调的RoBERTa大型模型实现了高精度,使得DOH资源的可行评估.
科学领域:
- 计算语言学 计算语言学
- 公共卫生信息学 公共卫生信息学
- 卫生沟通 卫生沟通
背景情况:
- 国家卫生部 (DOH) 网站是人类乳头瘤病毒 (HPV) 健康信息的关键来源,影响公众意识和疫苗接种决策.
- 系统地评估这些网站上HPV疫苗接种信息的质量,完整性和激励影响至关重要,但具有挑战性.
研究的目的:
- 开发一个计算效率高的框架来评估HPV疫苗接种信息的质量和影响,并在州卫生部网站上.
- 从48个卫生部网站创建一个综合数据集和一个用于模型培训和评估的注释数据集.
主要方法:
- 使用大型语言模型 (LLM) 和RoBERTa Large的知识蒸框架的开发.
- 创建一个注释数据集 (n=400) 来自48个州卫生部网站,重点关注HPV和HPV疫苗接种.
- 培训和评估高效的学生模型,包括微调的RoBERTa Large,用于内容评估.
主要成果:
- 微调的RoBERTa大型车在测试组中获得了0.74的高F1得分,超过了其他学生车型.
- 表现最好的模型的表现接近于教师模型 (F1=0.77).
- 该模型已成功部署,用于对国家卫生部网站内容进行计算可行的评估.
结论:
- 一个微调的RoBERTa大模型提供了一种有效和高效的方法来评估HPV疫苗接种信息的质量在州卫生部网站.
- 该框架支持系统评估,有可能改善关于HPV疫苗接种的公共卫生沟通策略.
- 该研究强调了LLM和蒸对于分析健康信息存储库的实用性,并讨论了更广泛的影响.
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