没有接地的大型语言模型可以恢复人类概念的非传感运动,但不是传感运动特征
Qihui Xu1, Yingying Peng2, Samuel A Nastase3
1Department of Psychology, Ohio State University, Columbus, OH, USA. xu.5430@osu.edu.
Nature human behaviour
|June 4, 2025
概括
大型语言模型 (LLM) 显示有限的人类类概念表示,特别是在感官和运动领域. 视觉学习改善了法学士与人类概念表征的对齐.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 越来越复杂,引发了关于它们形成复杂概念表示的能力的问题.
- 对概念形成的多感官体验基础的必要性仍然是一个关键的辩论.
- 没有基础的LLM挑战了关于体现经验在认知中的作用的传统观点.
研究的目的:
- 调查LLM能够在多大程度上产生复杂的概念表示.
- 为了比较人类和最先进的法学士之间的概念表征.
- 确定视觉学习对LLM概念表示与人类对齐的影响.
主要方法:
- 对比了4442个词汇概念的多维表示.
- 利用了人类数据集 (格拉斯哥规范,兰开斯特规范) 和具有/没有视觉学习的LLM.
- 分析了跨非感觉运动,感觉和运动领域的表征.
主要成果:
- 在LLM和人类表现之间的相似性从非感觉运动领域减少到感觉和运动领域,表明差异.
- 具有视觉学习的LLM在与视觉相关的维度中与人类的表现有增强的相似性.
- 观察到有系统的分歧,特别是在运动领域.
结论:
- 语言本身可能会对LLMs在实现类似人类的概念表征方面有局限性.
- 整合不同的模式,如视觉学习,可以提高法学士与人类概念系统的一致性.
- 多感官体验可能对开发强大的,类似人类的AI概念理解至关重要.
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