符号不接地:大型语言模型的成功 (和失败) 揭示了关于人类认知的东西
1Department of Philosophy, University of Louisville, Louisville, KY, USA.
概括
大型语言模型 (LLM) 显示语言模型.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 大型语言模型 (LLM) 在自然语言处理方面表现出色,促使与人类语义理解进行比较.
- 嵌入式认知理论认为,语义表示需要感官运动接地,这是基于文本的LLM缺乏的特征.
研究的目的:
- 研究LLM语义能力和体现认知之间的关系.
- 探索语言本身如何有助于语义理解,即使没有直接的感觉运动体验.
主要方法:
- 对LLM语义处理的比较分析与认知科学研究的发现.
- 嵌入式认知原理与人工智能能力的理论整合.
主要成果:
- 人类的认知整合了有形和人工智能类处理.
- 语义记忆显示了感觉运动系统和语言学习中的部分接地.
- LLM强调了语言在提供语义信息方面的丰富性.
结论:
- 语言经验可以支和扩展人类的感知能力.
- 在体现的认知中,语言为非接地认知提供了一条途径.
- LLM 证明了语言数据对语义表示的力量.
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