# Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models

arXiv: https://arxiv.org/abs/2311.04915 · HTML: https://mlsanity.com/paper/2311.04915/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [GPT Takes the Bar Exam](https://mlsanity.com/paper/2212.14402/) (2022)
- [Modeling Empathy and Distress in Reaction to News Stories](https://mlsanity.com/paper/1808.10399/) (2018)
- [An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing](https://mlsanity.com/paper/2309.08008/) (2023)
- [Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models](https://mlsanity.com/paper/2209.08141/) (2022)
- [Digital Therapeutics for Mental Health: Is Attrition the Achilles Heel?](https://mlsanity.com/paper/2207.05179/) (2022)

## Built on by (most cited first, 24 of 24)

- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://mlsanity.com/paper/2404.15676/) (2024)
- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification](https://mlsanity.com/paper/2403.14255/) (2024)
- [HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue](https://mlsanity.com/paper/2601.19922/) (2026)
- [AI with Emotions: Exploring Emotional Expressions in Large Language Models](https://mlsanity.com/paper/2504.14706/) (2025)
- [OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model](https://mlsanity.com/paper/2507.05177/) (2025)
- [Rational Sensibility: LLM Enhanced Empathetic Response Generation Guided by Self-presentation Theory](https://mlsanity.com/paper/2312.08702/) (2023)
- [SYNTHEMPATHY: A Scalable Empathy Corpus Generated Using LLMs Without Any Crowdsourcing](https://mlsanity.com/paper/2502.17857/) (2025)
- [The Pursuit of Empathy: Evaluating Small Language Models for PTSD Dialogue Support](https://mlsanity.com/paper/2505.15065/) (2025)
- ["I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support](https://mlsanity.com/paper/2405.13803/) (2024)
- [Integrating Physiological Data with Large Language Models for Empathic Human-AI Interaction](https://mlsanity.com/paper/2404.15351/) (2024)
- [RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems](https://mlsanity.com/paper/2509.10746/) (2025)
- [A Generic Review of Integrating Artificial Intelligence in Cognitive Behavioral Therapy](https://mlsanity.com/paper/2407.19422/) (2024)
- [Mixed Chain-of-Psychotherapies for Emotional Support Chatbot](https://mlsanity.com/paper/2409.19533/) (2024)
- [Somatic in the East, Psychological in the West?: Investigating Clinically-Grounded Cross-Cultural Depression Symptom Expression in LLMs](https://mlsanity.com/paper/2508.03247/) (2025)
- [Empathy-R1: A Chain-of-Empathy and Reinforcement Learning Framework for Long-Form Mental Health Support](https://mlsanity.com/paper/2509.14851/) (2025)
- [From Measurement to Expertise: Empathetic Expert Adapters for Context-Based Empathy in Conversational AI Agents](https://mlsanity.com/paper/2511.03143/) (2025)
- [Bridging Cognition and Emotion: Empathy-Driven Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.17332/) (2025)
- [Why We Need a New Framework for Emotional Intelligence in AI](https://mlsanity.com/paper/2512.23163/) (2025)
- [EmoLLM: Appraisal-Grounded Cognitive-Emotional Co-Reasoning in Large Language Models](https://mlsanity.com/paper/2603.16553/) (2026)
- [Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets](https://mlsanity.com/paper/2607.03540/) (2026)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)

## Abstract (arXiv)

We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce Large Language Models (LLMs) to reason about human emotional states. This method is inspired by various psychotherapy approaches including Cognitive Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality Therapy (RT), each leading to different patterns of interpreting clients' mental states. LLMs without reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathetic responses aligned with the different reasoning patterns of each psychotherapy model. The CBT based CoE resulted in the most balanced generation of empathetic responses. The findings underscore the importance of understanding the emotional context and how it affects human and AI communication. Our research contributes to understanding how psychotherapeutic models can be incorporated into LLMs, facilitating the development of context-specific, safer, and empathetic AI.