情感概念知识的语言特定表示因果支持情感推断推断
Ming Li1,2, Yusheng Su3, Hsiu-Yuan Huang4
1Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China.
iScience
|December 13, 2024
概括
大型语言模型 (LLM) 表明,基于语言的情感概念因果帮助人工智能理解新的情感情况. 这表明语言对于情感推断至关重要,即使没有感官输入.
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:
- 语言是人类情感体验和沟通的核心.
- 语言在塑造情感理解中的作用仍在争论中.
- 人工智能,特别是大型语言模型 (LLM),为研究这些问题提供了一种新的方法.
研究的目的:
- 调查基于语言的情感表现是否会因果影响人工智能推断情感意义的能力.
- 为了确定LLM是否可以在没有感官运动接地的情况下学习和利用情感概念.
- 探索语言衍生情感知识对生成情感推理的贡献.
主要方法:
- 使用大型语言模型 (LLM) 作为计算工具.
- 在法学士课程中确定了不同的人工神经元群体,代表了人类情感概念的14个属性.
- 操纵这些与属性相关的神经元来评估它们在情绪推断中的因果作用.
主要成果:
- 通过使用不同的神经群体,LLM代表了人类情感概念属性.
- 操纵这些神经群体表明了情感概念知识和产生情感推理之间的因果关系.
- 绩效下降与人类心理空间中特定属性的重要性有关.
结论:
- 士可以独立于感官运动体验来学习情绪.
- 语言衍生的情感概念知识对人工智能对情感推断的能力作出了重大贡献.
- 这些发现为语言在情感理解中的因果作用提供了概念证明.
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