评估一个高度专业的主题的大型语言模型,辐射瘤学物理学
Jason Holmes1, Zhengliang Liu2, Lian Zhang1
1Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States.
Frontiers in oncology
|August 2, 2023
概括
大型语言模型 (LLM) 在辐射瘤物理学方面表现有前途. 聊天GPT GPT-4表现出色,展示了作为专家助理的潜力,尽管人类协作改善了结果.
科学领域:
- 医学物理 医学物理
- 人工智能的人工智能
- 辐射瘤学 辐射瘤学
背景情况:
- 在标准化测试中评估大型语言模型 (LLM) 可能并不反映它们的真正能力.
- 辐射瘤学物理学为LLM评估提供了一个专门的,相关的领域.
研究的目的:
- 评估LLM在回答辐射瘤物理问题的表现.
- 建立一个专业科学领域的LLM能力的基准.
主要方法:
- 开发了一个100个问题的辐射瘤学物理考试.
- 对四个LLM (ChatGPT GPT-3.5,ChatGPT GPT-4,Bard,BLOOMZ) 与医学物理学家和非专家进行了评估.
- 评估了LLM的演推理和协作潜力.
主要成果:
- 当被要求首先解释答案时,ChatGPT GPT-4的表现优于其他LLM和人群.
- 聊天GPT模型显示了高响应一致性;巴德没有.
- 聊天GPT GPT-4显示出新兴的演推理能力.
- 医学物理学家的表现明显超过了使用多数票的ChatGPT GPT-4.
结论:
- LLM,特别是ChatGPT GPT-4,显示出作为辐射瘤学专家助理的巨大潜力.
- 人类的协作,通过多数投票,提高了超出个人LLM能力的表现.
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