Exploring the Effect of Robotic Embodiment and Empathetic Tone of LLMs on Empathy Elicitation
By: Liza Darwesh , Jaspreet Singh , Marin Marian and more
Potential Business Impact:
Robots can't make you care about others.
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.
Similar Papers
Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Human-Computer Interaction
Teaches computers to listen with feelings.
Exploring LLM-generated Culture-specific Affective Human-Robot Tactile Interaction
Human-Computer Interaction
Robots learn to touch people in different ways.
Mitigating the Uncanny Valley Effect in Hyper-Realistic Robots: A Student-Centered Study on LLM-Driven Conversations
Human-Computer Interaction
Makes creepy robots feel more friendly and real.