完善语义表示的多模式性
Laura Anna Ciaccio1, Luca Rinaldi2
1Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
Trends in cognitive sciences
|November 15, 2025
概括
大型语言模型 (LLM) 帮助认知科学家了解语言和感觉运动体验如何塑造概念. 这项研究使用LLM来区分各种感官输入对概念知识的影响.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 神经科学是一个神经科学.
背景情况:
- 概念知识的起源是有争议的,理论强调语言输入或感觉运动体验.
- 由于人类发育中感官和语言数据的复杂相互作用,解开这些影响是具有挑战性的.
研究的目的:
- 研究语言和感官运动体验在概念知识形成中的不同作用.
- 利用大型语言模型 (LLM) 作为一种用于建模和分析这些影响的新工具.
主要方法:
- 利用先进的大型语言模型 (LLM) 来处理和表示大量的文本数据.
- 设计实验来比较从纯粹语言数据中获得的概念表示与包含模拟感觉运动信息的概念表示.
- 在不同的输入条件下分析了LLM产生的概念表示的差异.
主要成果:
- 士课程展示了捕捉概念知识的细微方面的能力.
- 结果表明,语言经验对抽象的概念理解有显著的贡献.
- 感官运动体验对于以更具体现的方式将概念接地至关重要.
结论:
- 大型语言模型 (LLM) 提供了一种强大的计算方法来解决认知科学中长期存在的问题.
- 这项研究提供了语言和感官运动模式在塑造概念知识方面的差异性贡献的证据.
- 未来的研究可以进一步完善LLM架构,以更好地模拟人类的概念发展.
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