Is ChatGPT More Empathetic than Humans?
Not on the map. Why?
Abstract (arXiv)
This paper investigates the empathetic responding capabilities of ChatGPT, particularly its latest iteration, GPT-4, in comparison to human-generated responses to a wide range of emotional scenarios, both positive and negative. We employ a rigorous evaluation methodology, involving a between-groups study with 600 participants, to evaluate the level of empathy in responses generated by humans and ChatGPT. ChatGPT is prompted in two distinct ways: a standard approach and one explicitly detailing empathy's cognitive, affective, and compassionate counterparts. Our findings indicate that the average empathy rating of responses generated by ChatGPT exceeds those crafted by humans by approximately 10%. Additionally, instructing ChatGPT to incorporate a clear understanding of empathy in its responses makes the responses align approximately 5 times more closely with the expectations of individuals possessing a high degree of empathy, compared to human responses. The proposed evaluation framework serves as a scalable and adaptable framework to assess the empathetic capabilities of newer and updated versions of large language models, eliminating the need to replicate the current study's results in future research.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 5 of its 53 references that are in the map. First posted on arXiv 22 Feb 2024.
Builds on
5 of 53 references.
References · 5
- 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
- 2020A Taxonomy of Empathetic Response Intents in Human Social Conversationscited by 51
- 2023Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvementscited by 42
- 2023Boosting Distress Support Dialogue Responses with Motivational Interviewing Strategycited by 15
- 2023Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generationcited by 9
Built on by
Citing papers per year: 2024: 3; 2025: 3; 2026: 2.
8 papers.
- 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspectivecited by 25
- 2026HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialoguecited by 13
- 2025Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queriescited by 6
- 2024Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialoguescited by 3
- 2024The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skillscited by 1
- 2025Personality Editing for Language Models through Adjusting Self-Referential Queriescited by 1
- 2025Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarizationcited by 0
- 2026Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contextscited by 0
- 2026Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts7 May 2026
- 2026HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue9 Jan 2026
- 2025Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization4 Sep 2025
- 2025Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries12 Apr 2025
- 2025Personality Editing for Language Models through Adjusting Self-Referential Queries17 Feb 2025
- 2024Scoring with Large Language Models: A Study on Measuring Empathy of Responses in Dialogues28 Dec 2024
- 2024The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills21 Sep 2024
- 2024Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective30 Jul 2024