聊天GPT参加了DFPH考试:大型语言模型的性能和支持公共卫生学习的潜力
Nathan P Davies1, Robert Wilson2, Madeleine S Winder3
1Nottingham Centre for Public Health and Epidemiology, University of Nottingham, Nottingham City Hospital, Hucknall Rd, Nottingham, NG5 1PB, England. Nathan.davies@nottingham.ac.uk.
BMC medical education
|January 11, 2024
概括
在公共卫生考试中,ChatGPT表现出强的表现,超过了人类的通过率,并展示了先进的学习能力. 然而,其事实上的错误和类似人类的反应给教育使用带来了挑战.
科学领域:
- 人工智能在健康职业教育教育中的应用
- 公共卫生检查评估评估
- 大型语言模型的性能评估.
背景情况:
- 人工智能 (AI) 和像ChatGPT这样的大型语言模型 (LLM) 越来越多地被评估为医疗保健应用.
- 在公共卫生专业考试中使用LLM仍然未被探索.
- 本研究评估了ChatGPT在公共卫生学院外交官考试 (DFPH) 的一个组成部分上的表现.
研究的目的:
- 评估ChatGPT在公共卫生学院外交官 (DFPH) 考试中的表现.
- 评估ChatGPT提供的理解程度和独特的见解.
- 为了比较ChatGPT与人类考官的表现和通过率.
主要方法:
- 聊天GPT在过去的DFPH考试中对119个公开可用的问题进行了测试.
- 两名DFPH审查员评估了ChatGPT的准确性,洞察力和学习能力.
- 绩效指标包括通过率,识别人工智能生成答案的准确性以及每个问题的独特见解.
主要成果:
- 聊天GPT通过了4个DFPH论文中的3个,超过了典型的通过率.
- 该模型在研究方法问题上表现出色,并显示出高答案准确度.
- 审查人员确定了ChatGPT的答案准确率为73.6%,而人类答案的准确率为28.6%,ChatGPT为每个问题提供了3.6个独特的见解.
结论:
- 在公共卫生方面,LLM显示出作为教育工具的巨大潜力.
- 由于潜在的错误性,LLMs的事实准确性需要仔细考虑.
- 将人工智能产生的内容与人类反应区分开来,对学术完整性和学习构成挑战.
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