大型语言模型擅长解决和创建情绪智力测试
Katja Schlegel1,2, Nils R Sommer3, Marcello Mortillaro4
1Institute of Psychology, University of Bern, Bern, Switzerland. Katja.schlegel@unibe.ch.
Communications psychology
|May 21, 2025
概括
大型语言模型 (LLM) 在情绪智能测试中显示出高精度,超过了人类的表现. 此外,LLM还可以生成新的测试项目,其难度与现有的测试项目相似.
科学领域:
- 人工智能的人工智能
- 心理学 心理学 心理学
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 展示了广泛的专业知识,但它们的情绪智能能力在很大程度上尚未被探索.
- 在AI中评估情绪智能对于理解人与人工智能的互动以及人工智能在敏感应用中的作用至关重要.
研究的目的:
- 调查LLM是否可以准确地解决基于表现的情绪智力测试.
- 评估LLM能够产生新的情绪智能测试项目的能力.
主要方法:
- 五个LLM (ChatGPT-4,ChatGPT-o1,Gemini 1.5闪光,Copilot 365,Claude 3.5海库,DeepSeek V3) 在标准情商评估中进行了测试.
- 聊天GPT-4为这些测试生成了新的项目,然后在五项研究中对467名人类参与者进行了测试.
主要成果:
- 在情绪智能测试中,LLM的平均准确率为81%,远远超过人类平均水平的56%.
- 通过LLM生成的测试项目显示出与原始测试相等的统计学难度.
- 虽然在原始和生成的测试中,一些心理测量属性略有差异,但效果大小小小,表明有很强的相关性.
结论:
- 法律学士表现出理解和响应情绪智力评估的强大能力.
- 简单的LLM可以产生情感相关的测试内容,其难度与人类创建的测试相当.
- 这些发现表明,LLM拥有对人类情绪及其调节的准确知识.
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