将多式定向人际同步融入同情反应生成中
Jingyu Quan1, Yoshihiro Miyake1, Takayuki Nozawa2
1Department of Computer Science, Institute of Science Tokyo, Yokohama 226-8502, Japan.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究表明,将人际同步纳入人工智能模型可以增强同情反应. 积极的同步增强了情绪反应,而积极和消极的同步改善了AI通信中的解释.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 情感计算是一种情感计算.
背景情况:
- 人际同步,个人之间的协调,对于有效的人际沟通和同理心至关重要.
- 现有的用于产生同情反应的AI模型往往缺乏对人际关系动态的细微理解.
- 建模多模式定向同步 (正和负) 提供了一种新的方法来增强AI的同理心.
研究的目的:
- 研究多式定向人际同步对人工智能产生的同情反应的影响.
- 提出和评估一个 AI 模型,结合基于 cosine 相似性的人际同步.
- 分析正负同步如何影响AI反应中的语言和同理心水平.
主要方法:
- 开发一种人工智能模型,使用共弦相似度整合多式定向人际同步 (正和负).
- 评估具有和没有同步特征的模型的比较实验.
- 在语言和同理心层面分析产生的反应.
主要成果:
- 结合多式联动同步的模型产生了更接近地面真相的响应,并表现出更大的多样性.
- 积极的同步与增强的情绪反应,减少探索和改进的解释有关.
- 负同步与AI响应中的探索减少和解释增加相关.
结论:
- 多模式定向的人际同步是改善AI同情反应生成的重要因素.
- 拟议的模型成功地整合了同步功能,从而实现了更类似人类和更富同情心的AI通信.
- 研究结果突出了正面和负面同步在AI的情感和认知处理对同情心的不同作用.
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