用大型语言模型在社交媒体中打击与健康相关的错误信息
Moisés Robles-Pagán1, Manuel Rodríguez-Martínez1
1University of Puerto Rico - Mayagüez, Mayagüez PR 00680, USA.
概括
大型语言模型 (LLM) 可以有效地打击社交媒体上的健康虚假信息. 精心调整的模型准确地分类与健康相关的内容,并识别虚假信息,帮助专家反驳努力.
科学领域:
- 人工智能的人工智能
- 公共卫生 公共卫生
- 计算语言学 计算语言学
背景情况:
- 社交媒体平台面临着健康错误信息的传播带来的重大挑战.
- 准确识别和反驳医学错误信息对于公共卫生至关重要.
- 应对在线健康错误信息的现有方法需要加强.
研究的目的:
- 微调大型语言模型 (LLM) 以识别和应对社交媒体上的与健康有关的虚假信息.
- 评估T5,BERT和LaMa-2模型在分类健康内容和检测虚假信息方面的表现.
- 开发一个利用Retrieval Augmented Generation (RAG) 来反驳被发现的错误信息的系统.
主要方法:
- 微调T5,BERT和LaMa-2模型在两个阶段:与健康相关的文本分类和虚假信息验证.
- 采用搜索增强生成 (RAG) 来查询可信的医疗数据库以获得准确的信息.
- 通过使用精度,回忆和F1分数来实验评估模型性能.
主要成果:
- 模型实现了94%的精度,95%的回忆率和90%的F1对与健康有关的内容进行分类.
- 虚假信息文本的分类准确率为99%,回忆率为95%,F1率为97%.
- 开发的系统在识别和分类健康虚假信息方面具有很高的准确性.
结论:
- 精心调整的LLM是打击社交媒体上的健康虚假信息的高效工具.
- 通过利用权威的医学来源,RAG方法可以有效地反驳错误信息.
- 本系统为卫生专家提供了一种有价值的解决方案,以管理在线与健康相关的错误信息.
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