人类和大型语言模型对语言认知障碍的判断:一种可解释的人工智能方法
Maya Zadok1, Lotem Peled-Cohen1, Nitay Calderon1
1Faculty of Data and Decisions Sciences Technion- Israel Institute for Technology Haifa Israel.
Alzheimer's & dementia (Amsterdam, Netherlands)
|January 26, 2026
概括
非专家的人类和大型语言模型 (LLM) 努力在文本中一致识别痴呆症线索. 通过使用比人类更广泛的语言信号,LLM显示出对痴呆症查的潜力.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 语言学的语言学.
背景情况:
- 人类对文本中痴呆症指标的感知对于早期医学评估至关重要.
- 大型语言模型 (LLM) 具有作为可访问的认知衰退查工具的潜力.
研究的目的:
- 为了比较非专家人类和LLM如何在转录文本中感知痴呆症相关的语言线索.
- 评估LLM作为潜在痴呆症查工具的有效性.
主要方法:
- 人类和LLM评估了转录的图片描述,以寻找痴呆症的迹象.
- 用专家指导的语言特征和后勤回归来分析文本.
主要成果:
- 人类的判断不一致,依赖于有限,有时不准确的线索.
- 与人类相比,LLM使用了比人类更全面和临床相关的特征集.
- 人类和LLM都在痴呆症检测中表现出虚假阴性结果的趋势.
结论:
- 需要加强人类和LLM对各种痴呆症相关语言信号的识别.
- 可解释的人工智能对于理解痴呆症相关的语言模式和改善早期检测是有价值的.
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