大型语言模型:历史和社会文化视角
1The Division of the Social Sciences, The University of Chicago.
Cognitive science
|March 19, 2024
概括
大型语言模型 (LLM) 通过信息理论和语言视角连接到认知科学. 整合元真实功能和索引性是人工智能充分掌握人类沟通细微差别的关键.
科学领域:
- 认知科学 认知科学
- 计算语言学 计算语言学
- 社会语言学 社会语言学
- 语言人类学语言人类学.
背景情况:
- 探讨大语言模型 (LLM) 和认知科学之间的历史和当代联系.
- 强调信息理论和统计学学习在人类沟通中的持久意义.
- 讨论自20世纪70年代以来社会语言学和语言人类学的演变.
研究的目的:
- 提出了超实用函数和索引性作为整合通信维度的关键概念.
- 旨在弥合人类沟通和人工智能交互的计算,认知和社会文化方面.
- 强调在当代认知科学中需要跨学科对话.
主要方法:
- 信息理论,统计语言模型和社会人类学语言理论的理论探索.
- 分析元实证函数和索引性.
- 文献综述和综合跨学科的观点.
主要成果:
- 士课程强调了20世纪中叶基于信息和统计学学习理论的重要性.
- 社会语言学和语言人类学提供了批判性的观点,丰富了理论框架.
- 超实用函数和索引性为整合语义,文本和实用沟通维度提供了一个框架.
结论:
- 当代的LLM还没有完全实现语义,文本和务实沟通的整合.
- 认知科学正处于十字路口,需要跨学科的合作.
- 弥合计算,认知和社会文化方面对于人与人工智能的互动至关重要,特别是对于先进的人工智能模型.
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