使用基于比喻和刺情景的精神病学查工具评估大型语言模型的能力
1Max-Planck Institute for Human Development, 14195 Berlin, Germany.
Journal of Intelligence
|July 26, 2024
概括
大型语言模型表现出更好的比喻理解,但与刺斗,类似于患有阿斯伯格综合征的儿童. 开发专门的AI培训对于细微的沟通能力至关重要.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 语言学的语言学.
背景情况:
- 隐喻和刺是人类社会沟通的关键方面.
- 患有阿斯伯格综合征的儿童经常表现出难以理解刺的困难.
- 以前的研究使用隐喻和刺理解测试来区分阿斯伯格综合征.
研究的目的:
- 评估大型语言模型 (LLM) 对微妙的人类沟通的理解,特别是隐喻和刺.
- 评估模型参数对LLM理解这些复杂语言元素的能力的影响.
主要方法:
- 在最近的LLMs中使用标准化测试来评估隐喻和刺的理解.
- 分析了LLM大小 (参数) 和两种理解任务的表现之间的关系.
主要成果:
- 在LLM中,表现出比喻理解能力的提高,与模型参数的增加相关.
- 在较大的模型大小下,没有观察到刺语理解的显著改善.
- 关于刺的LLM表现并不反映在人类发展中看到的模式.
结论:
- 当前的LLM架构可能需要不同的培训策略来开发强大的刺理解能力.
- 以认知为基础的方法,可能受到像杏仁体这样的人类情感学习中心的启发,对于人工智能来说,理解刺是必要的.
- 需要进一步的研究来弥合LLM对微妙的社会沟通线索的理解差距.
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