用机器人或聊天机器人测试共情效果，发现外形和语气对助人意愿影响不大。
Exploring the Effect of Robotic Embodiment and Empathetic Tone of LLMs on Empathy Elicitation
- 让机器人或语音聊天机器人以共情或中立语气与人互动
- 60人参与，共情语气未提升志愿时长，机器人外形也无显著影响
- 适合关注人机交互、情感计算的研究者阅读
本研究探讨通过与社交代理互动，激发对第三方的共情。60名参与者与搭载大语言模型（LLM）的实体机器人或语音聊天机器人进行互动，后者被设置为展现共情语气或保持中立。互动围绕虚构人物Katie Banks展开，她身处困境且急需资金援助。通过参与者愿为Katie志愿服务的时长，以及对代理的认知评价来评估共情效果。结果表明，机器人具身化或共情语气均未显著影响参与者助人意愿。尽管LLM能有效模拟人类共情，但促使参与者产生真实共情反应仍具挑战性。
原文摘要 · Abstract (English)
This study investigates the elicitation of empathy toward a third party through interaction with social agents. Participants engaged with either a physical robot or a voice-enabled chatbot, both driven by a large language model (LLM) programmed to exhibit either an empathetic tone or remain neutral. The interaction is focused on a fictional character, Katie Banks, who is in a challenging situation and in need of financial donations. The willingness to help Katie, measured by the number of hours participants were willing to volunteer, along with their perceptions of the agent, were assessed for 60 participants. Results indicate that neither robotic embodiment nor empathetic tone significantly influenced participants' willingness to volunteer. While the LLM effectively simulated human empathy, fostering genuine empathetic responses in participants proved challenging.
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