针对自我治疗知识的定制GPT和GPT-4o的比较:使用日本注册销售人员考试的可行性研究
Shota Okazaki1, Yuichi Mine2,3, Saori Takeda2,3
1Advanced Intelligence Technology Center, Sapporo City University, Sapporo, Hokkaido, Japan.
Digital health
|February 24, 2026
概括
最小量身定制的大型语言模型 (LLM) 在日本注册销售人员考试中显著提高了绩效,表明了自我治疗支持的潜力. 为了安全性和实用性,需要进一步进行现实世界的测试.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 制药科学 制药科学
背景情况:
- 在日本,注册销售人员指导消费者选择非处方药和安全使用.
- 评估人工智能 (AI) 模型的知识水平对于它们在消费者健康支持中的应用至关重要.
- 大型语言模型 (LLM) 是有前途的,但需要对专业知识领域进行评估.
研究的目的:
- 确定最小定制的LLM是否可以达到适合自我治疗支持的知识水平.
- 评估LLM在日本注册销售人员考试中的表现,这是OTC药物知识的代理.
- 通过简单的参考材料上传来探索LLM定制,而不需要复杂的编程.
主要方法:
- 2024年日本注册销售员考试 (838个选择题) 作为基准.
- 通过将官方考试指南PDF上传到GPT-4o,创建了定制的GPT.
- 标准GPT-4o和定制GPT都使用零射击方法进行了评估,没有浏览网络.
主要成果:
- 与标准GPT-4o (78.40%) 相比,定制GPT的整体准确性 (92.36%) 显著提高 (p < 0.001).
- 定制的LLM在所有五个考试领域都表现出卓越的表现 (p < 0.05).
- 通过文件上传进行最小的定制,提高了LLM知识评估绩效.
结论:
- 简单的LLM与参考材料的定制可以提高基于知识的评估的性能.
- 虽然有希望,但考试成绩是准备自我治疗支持的间接衡量标准.
- 未来的研究必须在现实场景中验证LLM的性能,重点关注实际实用性和安全性.
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