mlsanityData to 7 Oct 2026

The Illusion of Empathy: How AI Chatbots Shape Conversation Perception

arXiv 2411.12877 ↗First posted 19 Nov 2024cs.HC cs.AIcited by 12

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Abstract (arXiv)

As AI chatbots increasingly incorporate empathy, understanding user-centered perceptions of chatbot empathy and its impact on conversation quality remains essential yet under-explored. This study examines how chatbot identity and perceived empathy influence users' overall conversation experience. Analyzing 155 conversations from two datasets, we found that while GPT-based chatbots were rated significantly higher in conversational quality, they were consistently perceived as less empathetic than human conversational partners. Empathy ratings from GPT-4o annotations aligned with user ratings, reinforcing the perception of lower empathy in chatbots compared to humans. Our findings underscore the critical role of perceived empathy in shaping conversation quality, revealing that achieving high-quality human-AI interactions requires more than simply embedding empathetic language; it necessitates addressing the nuanced ways users interpret and experience empathy in conversations with chatbots.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 12 of its 50 references that are in the map. First posted on arXiv 19 Nov 2024.

Builds on

12 of 50 references.

References · 12

  1. 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 651
  2. 2020MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfercited by 443
  3. 2020A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Supportcited by 182
  4. 2020MIME: MIMicking Emotions for Empathetic Response Generationcited by 114
  5. 2020Recognizing Emotion Cause in Conversationscited by 66
  6. 2023Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvementscited by 42
  7. 2024Large Language Models Produce Responses Perceived to be Empathiccited by 36
  8. 2020Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategiescited by 29
  9. 2022Empathic Conversations: A Multi-level Dataset of Contextualized Conversationscited by 20
  10. 2024Are Large Language Models More Empathetic than Humans?cited by 18
  11. 2024Multi-dimensional Evaluation of Empathetic Dialog Responsescited by 10
  12. 2022Computer says "No": The Case Against Empathetic Conversational AIcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 10.

12 papers.

  1. 2025Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of Designcited by 11
  2. 2025SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversationscited by 8
  3. 2026PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Modelscited by 5
  4. 2026MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialoguecited by 2
  5. 2026Discourse Diversity in Multi-Turn Empathic Dialoguecited by 1
  6. 2026Dynamic In-Group Persona Generation for Enhancing Human-AI Rapportcited by 1
  7. 2026RE-LLM: Refining Empathetic Speech-LLM Responses by Integrating Emotion Nuancecited by 0
  8. 2026EthicMind: A Risk-Aware Framework for Ethical-Emotional Alignment in Multi-Turn Dialoguecited by 0
  9. 2026Appropriateness of Empathy in AI: A Signal-Cost Perspectivecited by 0
  10. 2026TA-RAG: Tone-Aware Retrieval-Augmented Generation for Peer-Support Health Communicationcited by 0
  11. 2026TA-RAG: Tone Awareness as a Design Imperative for Retrieval-Augmented Generationcited by 0
  12. 2026How to Run Statistics over LLM Judges and Trust the Results: Calibrated Inference for Small-Sample AI Evaluation with evalstatscited by 0