年龄与大型语言模型对认知障碍的机器敏感性相比:横截面分析
Roy Dayan1,2, Benjamin Uliel1,2, Gal Koplewitz3,4
1Department of Neurology, Hadassah Medical Center, Jerusalem, Israel.
概括
领先的大型语言模型在蒙特利尔认知评估 (MoCA) 上显示认知障碍,较旧的模型表现更差. 这表明人工智能可能不会在诊断中取代人类医生.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 神经学 神经学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于各种领域,包括医疗保健.
- 评估LLM的认知能力对于理解其局限性和可靠性至关重要.
研究的目的:
- 用蒙特利尔认知评估 (MoCA) 和其他认知测试来评估领先的LLM的认知表现.
- 在这些AI模型中识别潜在的认知障碍.
主要方法:
- 对公开可用的LLMs进行横截面分析:ChatGPT版本4和4o,克劳德3.5索内特和双子座版本1和1.5.5.
- 通过基于文本的提示来执行MoCA测试 (8.1版本) 和额外的视觉空间/执行和Stroop测试.
- 评分遵循官方指导方针,并由神经科医生进行评估.
主要成果:
- 在MoCA上,ChatGPT 4o得分最高 (26/30),而Gemini 1.0得分最低 (16/30).
- 所有LLM在视觉空间/执行任务中表现不佳;双子座模型失败了延迟回忆.
- 只有ChatGPT 4o成功完成了Stroop不相称的阶段.
结论:
- 大多数LLM,除了ChatGPT 4o,在MoCA上表现出轻度认知障碍的迹象.
- 模型"年龄"与表现相关,类似于人类认知能力下降.
- 在LLM的认知缺陷引发了人们对其在医学诊断和患者信任方面的可靠性的担忧.
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