# Are Large Language Models More Empathetic than Humans?

arXiv: https://arxiv.org/abs/2406.05063 · HTML: https://mlsanity.com/paper/2406.05063/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [A Taxonomy of Empathetic Response Intents in Human Social Conversations](https://mlsanity.com/paper/2012.04080/) (2020)
- [Harnessing the Power of Large Language Models for Empathetic Response Generation: Empirical Investigations and Improvements](https://mlsanity.com/paper/2310.05140/) (2023)
- [Large Language Models Produce Responses Perceived to be Empathic](https://mlsanity.com/paper/2403.18148/) (2024)
- [An In-depth Look at Gemini's Language Abilities](https://mlsanity.com/paper/2312.11444/) (2023)
- [Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support](https://mlsanity.com/paper/2310.08017/) (2023)
- [Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation](https://mlsanity.com/paper/2308.00085/) (2023)

## Built on by (most cited first, 18 of 18)

- [Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective](https://mlsanity.com/paper/2504.10839/) (2025)
- [The Illusion of Empathy: How AI Chatbots Shape Conversation Perception](https://mlsanity.com/paper/2411.12877/) (2024)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations](https://mlsanity.com/paper/2509.16437/) (2025)
- [Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots](https://mlsanity.com/paper/2410.02783/) (2024)
- [RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools](https://mlsanity.com/paper/2503.21888/) (2025)
- [Being Kind Isn't Always Being Safe: Diagnosing Affective Hallucination in LLMs](https://mlsanity.com/paper/2508.16921/) (2025)
- [AI generates well-liked but templatic empathic responses](https://mlsanity.com/paper/2604.08479/) (2026)
- [From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models](https://mlsanity.com/paper/2506.02431/) (2025)
- [Anthropomorphism and Trust in Human-Large Language Model interactions](https://mlsanity.com/paper/2604.15316/) (2026)
- [Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning](https://mlsanity.com/paper/2506.11376/) (2025)
- [Conflicts Make Large Reasoning Models Vulnerable to Attacks](https://mlsanity.com/paper/2604.09750/) (2026)
- [Distilling Empathy from Large Language Models](https://mlsanity.com/paper/2507.08151/) (2025)
- [E-THER: A Multimodal Dataset for Empathic AI -- Towards Emotional Mismatch Awareness](https://mlsanity.com/paper/2509.02100/) (2025)
- [Theory Trace Card: Theory-Driven Socio-Cognitive Evaluation of LLMs](https://mlsanity.com/paper/2601.01878/) (2026)
- [ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts](https://mlsanity.com/paper/2602.08371/) (2026)
- [RE-LLM: Refining Empathetic Speech-LLM Responses by Integrating Emotion Nuance](https://mlsanity.com/paper/2602.10716/) (2026)
- [Empathy Is Not What Changed: Clinical Assessment of Psychological Safety Across GPT Model Generations](https://mlsanity.com/paper/2603.09997/) (2026)

## Abstract (arXiv)

With the emergence of large language models (LLMs), investigating if they can surpass humans in areas such as emotion recognition and empathetic responding has become a focal point of research. This paper presents a comprehensive study exploring the empathetic responding capabilities of four state-of-the-art LLMs: GPT-4, LLaMA-2-70B-Chat, Gemini-1.0-Pro, and Mixtral-8x7B-Instruct in comparison to a human baseline. We engaged 1,000 participants in a between-subjects user study, assessing the empathetic quality of responses generated by humans and the four LLMs to 2,000 emotional dialogue prompts meticulously selected to cover a broad spectrum of 32 distinct positive and negative emotions. Our findings reveal a statistically significant superiority of the empathetic responding capability of LLMs over humans. GPT-4 emerged as the most empathetic, marking approximately 31% increase in responses rated as "Good" compared to the human benchmark. It was followed by LLaMA-2, Mixtral-8x7B, and Gemini-Pro, which showed increases of approximately 24%, 21%, and 10% in "Good" ratings, respectively. We further analyzed the response ratings at a finer granularity and discovered that some LLMs are significantly better at responding to specific emotions compared to others. The suggested evaluation framework offers a scalable and adaptable approach for assessing the empathy of new LLMs, avoiding the need to replicate this study's findings in future research.