使用大型语言模型来估计多词表达式的特征:具体性,价值,兴奋性
Gonzalo Martínez1, Juan Diego Molero2, Sandra González2
1Universidad Carlos III de Madrid, Madrid, Spain.
Behavior research methods
|December 5, 2024
概括
大型语言模型 (LLM) 准确地估计了词的属性,如具体性,价值和唤起. 这些人工智能模型为研究人员选择研究刺激提供了有价值的数据集.
科学领域:
- 认知心理学 认知心理学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 估计单词的心理特性 (具体性,价值,唤起) 对于研究至关重要.
- 之前的AI方法与多词表达式的细微含义作斗争.
- 大型语言模型 (LLM) 为更准确的估计提供了潜力.
研究的目的:
- 评估LLM,特别是GPT-4o在预测具体性,价值和兴奋方面的有效性.
- 在单词和多词表达式上评估LLM绩效.
- 为研究人员提供可靠的LLM生成的规范数据集.
主要方法:
- 系统评估GPT-4o对具体性,价值和兴奋的预测能力.
- 对单词和多词表达式的相关性分析与人类评分.
- 与以前的AI模型进行比较分析.
主要成果:
- 对于多词表达式,GPT-4o与人类具体性评级 (r = .8) 显示出强烈的相关性.
- 在价值和兴奋方面,LLM的表现与单个单词的现有AI模型相匹配或超越.
- 通过LLM生成的估计显示,跨研究的多词表达式具有良好的有效性.
结论:
- 在精确估计心理词性质方面,LLM显著有前途.
- 开发的数据集为研究人员在刺激选择方面提供了宝贵的资源.
- 实际上,LLM可以有效地捕捉这些估计所需的语义细微差别.
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