mlsanityData to 7 Oct 2026

Exploring the Effect of Robotic Embodiment and Empathetic Tone of LLMs on Empathy Elicitation

arXiv 2503.20518 ↗First posted 26 Mar 2025cs.HC cs.CL cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 26 Mar 2025.

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References · 4

  1. 2019How should my chatbot interact? A survey on human-chatbot interaction designcited by 56
  2. 2023Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Supportcited by 17
  3. 2023Towards Designing a ChatGPT Conversational Companion for Elderly Peoplecited by 13
  4. 2024Robot Vulnerability and the Elicitation of User Empathycited by 2

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