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评估大型语言模型在构建一个结构化数据集从AskDocs的subreddit数据:方法论研究研究.

Quinn Snell1, Chase Westhoff1, John Westhoff2

  • 1Brigham Young University, 3361 TMCB, Provo, UT, 84602, United States, 1 8014225098.

Journal of medical Internet research
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PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 有效地从社交媒体中提取健康信息,与人类的准确性相匹配. 这验证了LLM用于分析数字健康通信和在线用户行为.

关键词:
在Reddit上,我们可以看到Reddit是什么.人工智能的人工智能是人工智能.数据提取数据提取.大型语言模型.非结构化的文本分析.

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科学领域:

  • 数字健康数字健康
  • 自然语言处理自然语言处理.
  • 计算社会科学 计算社会科学

背景情况:

  • 亚盘r/AskDocs是数字健康咨询的关键平台.
  • 分析来自r/AskDocs等论坛的非结构化用户生成内容是一项挑战.
  • 大型语言模型 (LLM) 为从社交媒体中提取健康信息提供了先进的工具.

研究的目的:

  • 评估LLM在将非结构化r/AskDocs数据转化为结构化格式中的有效性.
  • 将LLM数据提取性能与人类注释器进行比较.
  • 评估基于LLM的数据提取与人类认知过程的一致性.

主要方法:

  • 从2800个r/AskDocs帖子中提取数据,使用人类注释器 (医学学生) 和LLMs.
  • 人类注释包括人口统计,调查类型,代理关系,慢性疾病和咨询状态.
  • 在LLM数据提取中,使用Llama3,Genna和GPT等模型的工程提示 (JSON,几次拍摄) 进行数据提取;Cohen κ评估了注释器间的可靠性.

主要成果:

  • 拉玛370B (7个几次拍摄的例子) 和GPT-4 (2个几次拍摄的例子) 实现了最高的精度 (87.4%),与人类注释的黄金标准相比.
  • 拉玛370B在编码与健康有关的内容方面表现出卓越的表现.
  • 在从非结构化职位中提取人口统计信息方面,GPT-4表现出色.

结论:

  • 在从社交媒体健康论坛中提取人口统计和健康信息方面,LLM的表现与人类注释者相当.
  • 这项研究验证了LLM作为分析数字健康通信的可靠工具.
  • 通过了解在线行为和互动,LLM显示了在数字研究中推进方法的潜力.