使用大型语言模型作为一种可扩展的方法来理解公共卫生话语
Laura Espinosa1, Marcel Salathé1
1Digital Epidemiology Lab, School of Life Sciences, School of Computer and Communication Sciences, EPFL, Switzerland.
PLOS digital health
|October 14, 2024
概括
大型语言模型 (LLM) 从社交媒体上有效地衡量公众的疫苗接种立场. 性能最好的LLM提供了一个可扩展的公共卫生工具,尽管存在错误分类的风险与替代品.
科学领域:
- 公共卫生 公共卫生
- 计算语言学 计算语言学
- 社交媒体分析 社交媒体分析
背景情况:
- 在线公共卫生话语显著影响公共卫生动态.
- 大型语言模型 (LLM) 提供了一个可扩展的方法来分析广泛的在线文本数据.
研究的目的:
- 评估LLM在从社交媒体中提取公众对疫苗接种立场方面的有效性.
- 将LLM绩效与传统方法进行比较,并评估错误分类风险.
主要方法:
- 利用了专家注释的关于疫苗接种的社交媒体帖子数据集.
- 应用各种LLM (包括GPT和开源) 和基于规则的情绪分析工具.
- 对专家和众包注释进行了准确性评估.
主要成果:
- 顶级LLM的少数射击提示在立场分类中表现出卓越的表现.
- 所有替代方法都带有重大错误分类的重大风险.
- 在公共卫生领域,LLM为传统数据分析提供了可扩展和高效的替代方案.
结论:
- 公共卫生专业人员的LLM显示出很大的潜力,可以快速评估公众对卫生政策的看法.
- 将先进的LLM整合到公共卫生监测中可以加强对不断变化的公众态度的监测和响应.
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