在医疗咨询中对大型语言模型进行比较分析:专注于Helicobacter pylori感染
Qing-Zhou Kong1,2,3,4, Kun-Ping Ju1,2,3,4, Meng Wan1,2,3,4
1Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Helicobacter
|July 30, 2024
概括
大型语言模型 (LLM) 显示出对Helicobacter pylori感染的咨询有希望,但完整性需要改进. 英语回应通常优于中文,强调需要解决语言细微差别,以提高LLM可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 正在成为医疗咨询的潜在工具.
- 对于特定的健康状况,如Helicobacter pylori感染,LLM的可靠性和可行性需要彻底评估.
- 评估不同语言的LLM绩效对于全球适用性至关重要.
研究的目的:
- 评估三种流行的LLM (ChatGPT 4.0,ChatGPT 3.5,ERNIE Bot 4.0) 作为治疗Helicobacter pylori感染的辅导工具的可行性.
- 为了比较这些LLM在英语和中文辅导场景中的表现.
- 评估LLM产生的答案的准确性,完整性和可理解性.
主要方法:
- 三名LLM被询问了15个与Helicobacter pylori相关的英语和中文问题.
- 答案由三位独立审稿人使用利克特等级来对准确性,完整性和可理解性进行评分.
- 定义了可接受的值 (准确度≥4,完整性≥2,可理解性≥2) 用于响应评估.
主要成果:
- 总体平均分数表明高准确度 (4.80/6) 和可理解性 (2.90/3),但较低的完整性 (1.82/3).
- 可接受的答案比例为准确度为90%,完整度为45.6%,可理解性为100%.
- 与针对特定LLM的中国回复相比,英语回复显示出更好的完整性和准确性,在LLM之间没有发现任何显著差异.
结论:
- 关于H. pylori感染,LLM提供了令人满意的反应,特别是在准确性和可理解性方面.
- 提高LLM答案的完整性和可靠性对于它们在医疗咨询中的有效使用至关重要.
- 解决语言特异性的细微差别对于优化不同语言环境中的LLM绩效至关重要.
相关概念视频
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