探索大型语言模型的潜力,以从叙事中理解人际情感调节策略
Belén López-Pérez1, Yuhui Chen1, Xiuhui Li1
1School of Health Sciences, University of Manchester.
Emotion (Washington, D.C.)
|April 17, 2025
概括
人工智能 (AI) 和大型语言模型 (LLM) 显示出从叙述中分类人际情感调节策略的前景. 快速工程显著提高了人工智能的准确性,证明了这个研究领域的潜力.
科学领域:
- 心理学 心理学 心理学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 人际情绪调节策略通常通过问卷进行评估.
- 问卷可能不适合识字或内省能力有限的个人.
- 基于叙事的方法提供了一个替代方案,但需要进行密集的定性分析.
研究的目的:
- 评估使用人工智能和LLM用于从叙事数据中分类人际情感调节策略的可行性.
- 在识别这些策略时,将AI性能与人类编码进行比较.
- 探索快速工程对AI准确性的影响.
主要方法:
- 进行了两项研究,比较AI (ChatGPT,Claude) 与人类编码器在叙事数据上的比较.
- 分析了参与者详细描述人际情感调节的叙事反应.
- 快速工程技术被代地应用和评估.
主要成果:
- 最初的AI性能显示了与人类编码器的中度一致 (Kappa> .47).
- 精细的提示显著改善了人工智能与人类的一致性 (Kappa> .79在研究1中,Kappa> .76在研究2).
- 在不同的情绪和策略中观察到LLM绩效的变化.
结论:
- 人工智能,特别是LLM,可以成为分类人际情感调节策略的可行工具.
- 有效的快速工程和验证对于优化LLM性能至关重要.
- 在解释复杂的情感叙事方面,LLM既有优点,也有局限性.
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