在人机对话中使用大型语言模型实时生成情绪.
Chinmaya Mishra1,2, Rinus Verdonschot2, Peter Hagoort2,3
1Furhat Robotics AB, Stockholm, Sweden.
Frontiers in robotics and AI
|December 18, 2023
概括
社交机器人现在可以使用大型语言模型 (LLM) 来表达情绪,以改善人类联系. 这项研究表明,LLM控制的面部表情可以提高机器人的可爱性和在人机交互中的任务性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 人与机器人的交互
背景情况:
- 情感行为增强了社交机器人连接和内部状态的表达.
- 情绪对于在人机交互 (HRI) 中信号理解至关重要.
研究的目的:
- 利用大型语言模型 (LLM) 来控制机器人的情感行为.
- 通过对话历史和情绪评估实时预测机器人的情绪.
- 评估LLM驱动的一致面部表情对用户感知和任务执行的影响.
主要方法:
- 解释情绪评估作为一种情绪识别在对话 (ERC) 任务.
- 使用GPT-3.5来预测机器人从对话历史中转移情绪.
- 在一个用户研究中评估模型性能,比较一致的表达,没有情绪和不一致的表达.
主要成果:
- LLM可靠地产生了情绪,参与者可以感知到.
- 一致的,以模型为驱动的面部表情导致机器人被认为更像人类,情感上更合适.
- 参与者在一场用机器人表达式对应的卡片排序游戏中表现得更好.
结论:
- 通过LLM,可以实时可靠地控制机器人的情感行为.
- 一致的情绪表达增强了用户的感知和交互结果.
- 调查结果支持在治疗,陪伴和客户服务角色中将机器人纳入LLM整合.
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