# Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought

arXiv: https://arxiv.org/abs/2401.06836 · HTML: https://mlsanity.com/paper/2401.06836/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in Audio-language model reasoning, one of 17 directions in LLM reasoning.
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Placed in

- LLM reasoning / [Audio-language model reasoning](https://mlsanity.com/lr/audio-language-model-reasoning/)

## Builds on (most cited first, 20 of 20)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective](https://mlsanity.com/paper/2305.15408/) (2023)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [MISC: A MIxed Strategy-Aware Model Integrating COMET for Emotional Support Conversation](https://mlsanity.com/paper/2203.13560/) (2022)
- [BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://mlsanity.com/paper/2106.06169/) (2021)
- [Affect-LM: A Neural Language Model for Customizable Affective Text Generation](https://mlsanity.com/paper/1704.06851/) (2017)
- [Affect-Driven Dialog Generation](https://mlsanity.com/paper/1904.02793/) (2019)
- [SentiCap: Generating Image Descriptions with Sentiments](https://mlsanity.com/paper/1510.01431/) (2015)
- [DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversations](https://mlsanity.com/paper/2310.11374/) (2023)
- ["Factual" or "Emotional": Stylized Image Captioning with Adaptive Learning and Attention](https://mlsanity.com/paper/1807.03871/) (2018)
- [An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss](https://mlsanity.com/paper/1811.07078/) (2018)
- [UniSA: Unified Generative Framework for Sentiment Analysis](https://mlsanity.com/paper/2309.01339/) (2023)
- [Large language models for aspect-based sentiment analysis](https://mlsanity.com/paper/2310.18025/) (2023)
- [Customising General Large Language Models for Specialised Emotion Recognition Tasks](https://mlsanity.com/paper/2310.14225/) (2023)
- [End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis](https://mlsanity.com/paper/2111.02326/) (2021)

## Built on by (most cited first, 30 of 34)

- [Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks](https://mlsanity.com/paper/2408.03615/) (2024)
- [Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples](https://mlsanity.com/paper/2406.05673/) (2024)
- [Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective](https://mlsanity.com/paper/2408.04638/) (2024)
- [Evaluating Large Language Models with Psychometrics](https://mlsanity.com/paper/2406.17675/) (2024)
- [Evaluating Vision-Language Models for Emotion Recognition](https://mlsanity.com/paper/2502.05660/) (2025)
- [HiconAgent: History Context-aware Policy Optimization for GUI Agents](https://mlsanity.com/paper/2512.01763/) (2025)
- [Triangulating LLM Progress through Benchmarks, Games, and Cognitive Tests](https://mlsanity.com/paper/2502.14359/) (2025)
- [Affective Image Editing: Shaping Emotional Factors via Text Descriptions](https://mlsanity.com/paper/2505.18699/) (2025)
- [HCQA @ Ego4D EgoSchema Challenge 2024](https://mlsanity.com/paper/2406.15771/) (2024)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [A Review of Human Emotion Synthesis Based on Generative Technology](https://mlsanity.com/paper/2412.07116/) (2024)
- [From Rational Answers to Emotional Resonance: The Role of Controllable Emotion Generation in Language Models](https://mlsanity.com/paper/2502.04075/) (2025)
- [Towards Deeper Emotional Reflection: Crafting Affective Image Filters with Generative Priors](https://mlsanity.com/paper/2512.17376/) (2025)
- [Dynamic Generation of Personalities with Large Language Models](https://mlsanity.com/paper/2404.07084/) (2024)
- [EmPO: Emotion Grounding for Empathetic Response Generation through Preference Optimization](https://mlsanity.com/paper/2406.19071/) (2024)
- [Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI](https://mlsanity.com/paper/2502.11614/) (2025)
- [VIBE: Can a VLM Read the Room?](https://mlsanity.com/paper/2506.11162/) (2025)
- [EmoFSM: A Finite State Machine for Emotional Support Conversation](https://mlsanity.com/paper/2504.11837/) (2025)
- [An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3](https://mlsanity.com/paper/2506.00312/) (2025)
- [Evaluating Human-LLM Representation Alignment: A Case Study on Affective Sentence Generation for Augmentative and Alternative Communication](https://mlsanity.com/paper/2503.11881/) (2025)
- [EiCAP: Beyond Fluency, Probing and Improving Emotional Intelligence in LLMs via Psychologically Grounded Multi-Turn Dialogue](https://mlsanity.com/paper/2508.06196/) (2025)
- [Chain-of-Conceptual-Thought Elicits Daily Conversation in Large Language Models](https://mlsanity.com/paper/2510.18434/) (2025)
- [MeetingToM: Evaluating Multimodal LLMs on Theory-of-Mind Reasoning in Multi-Party Meetings](https://mlsanity.com/paper/2607.19235/) (2026)
- [PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models](https://mlsanity.com/paper/2409.15188/) (2024)
- [Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems](https://mlsanity.com/paper/2412.04492/) (2024)
- [Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.07794/) (2025)
- [Dual Information Speech Language Models for Emotional Conversations](https://mlsanity.com/paper/2508.08095/) (2025)
- [HEART: Emotionally-Driven Test-Time Scaling of Language Models](https://mlsanity.com/paper/2509.22876/) (2025)
- [Analyzing Image Beyond Visual Aspect: Image Emotion Classification via Multiple-Affective Captioning](https://mlsanity.com/paper/2511.23115/) (2025)
- [An Ubuntu-Guided Large Language Model Framework for Cognitive Behavioral Mental Health Dialogue](https://mlsanity.com/paper/2601.06875/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown remarkable performance in various emotion recognition tasks, thereby piquing the research community's curiosity for exploring their potential in emotional intelligence. However, several issues in the field of emotional generation tasks remain unresolved, including human preference alignment and emotional generation assessment. In this paper, we propose the Emotional Chain-of-Thought (ECoT), a plug-and-play prompting method that enhances the performance of LLMs on various emotional generation tasks by aligning with human emotional intelligence guidelines. To assess the reliability of ECoT, we propose an automated model-based evaluation method called Emotional Generation Score (EGS). EGS incorporates Goleman's Emotional Intelligence Theory as a consensus of human experts, providing a new perspective on the evaluation of emotional generation tasks. Extensive experimental results demonstrate the effectiveness of ECoT and EGS. Further, we discuss the promise of LLMs in the field of emotional intelligence and present key insights into the LLMs with the ECoT in emotional generation tasks.