# AI with Emotions: Exploring Emotional Expressions in Large Language Models

arXiv: https://arxiv.org/abs/2504.14706 · HTML: https://mlsanity.com/paper/2504.14706/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 41 references that are in the map.
- First posted on arXiv 20 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023)
- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [Large Language Models Understand and Can be Enhanced by Emotional Stimuli](https://mlsanity.com/paper/2307.11760/) (2023)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Evaluating Large Language Models on Controlled Generation Tasks](https://mlsanity.com/paper/2310.14542/) (2023)
- [Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives](https://mlsanity.com/paper/2401.02009/) (2024)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [Do Large Language Models know what humans know?](https://mlsanity.com/paper/2209.01515/) (2022)
- [Multilingual Language Models are not Multicultural: A Case Study in Emotion](https://mlsanity.com/paper/2307.01370/) (2023)
- [RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions](https://mlsanity.com/paper/2302.09466/) (2023)
- [Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models](https://mlsanity.com/paper/2311.04915/) (2023)
- [Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks](https://mlsanity.com/paper/2210.05664/) (2022)
- [From Text to Motion: Grounding GPT-4 in a Humanoid Robot "Alter3"](https://mlsanity.com/paper/2312.06571/) (2023)
- [Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence](https://mlsanity.com/paper/2402.10073/) (2024)
- [Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs](https://mlsanity.com/paper/2406.04460/) (2024)
- [A method for Selecting Scenes and Emotion-based Descriptions for a Robot's Diary](https://mlsanity.com/paper/2309.01951/) (2023)

## Built on by (most cited first, 11 of 11)

- [Emotion Concepts and their Function in a Large Language Model](https://mlsanity.com/paper/2604.07729/) (2026)
- [Do LLMs "Feel"? Emotion Circuits Discovery and Control](https://mlsanity.com/paper/2510.11328/) (2025)
- [Valence-Arousal Subspace in LLMs: Circular Emotion Geometry and Multi-Behavioral Control](https://mlsanity.com/paper/2604.03147/) (2026)
- [LongEmotion: Measuring Emotional Intelligence of Large Language Models in Long-Context Interaction](https://mlsanity.com/paper/2509.07403/) (2025)
- [Quantitative Introspection in Language Models: Tracking Emotive States Across Conversation](https://mlsanity.com/paper/2603.18893/) (2026)
- [Indirect tipping: a social attack surface in AI agent populations](https://mlsanity.com/paper/2609.25194/) (2026)
- [Bridging the behavior-neural gap: A multimodal AI reveals the brain's geometry of emotion more accurately than human self-reports](https://mlsanity.com/paper/2509.24298/) (2025)
- [Modeling Layered Consciousness with Multi-Agent Large Language Models](https://mlsanity.com/paper/2510.17844/) (2025)
- [Do Emotions in Prompts Matter? Effects of Emotional Framing on Large Language Models](https://mlsanity.com/paper/2604.02236/) (2026)
- [When AI Says It Feels](https://mlsanity.com/paper/2606.05734/) (2026)
- [How LLMs Build Fictional Worlds: Setting and Narrative Space in AI-Generated Creative Storytelling](https://mlsanity.com/paper/2609.02482/) (2026)

## Abstract (arXiv)

The human-level performance of Large Language Models (LLMs) across various tasks has raised expectations for the potential of Artificial Intelligence (AI) to possess emotions someday. To explore the capability of current LLMs to express emotions in their outputs, we conducted an experiment using several LLMs (OpenAI GPT, Google Gemini, Meta Llama3, and Cohere Command R+) to role-play as agents answering questions with specified emotional states. We defined the emotional states using Russell's Circumplex model, a well-established framework that characterizes emotions along the sleepy-activated (arousal) and pleasure-displeasure (valence) axes. We chose this model for its simplicity, utilizing two continuous parameters, which allows for better controllability in applications involving continuous changes in emotional states. The responses generated were evaluated using a sentiment analysis model, independent of the LLMs, trained on the GoEmotions dataset. The evaluation showed that the emotional states of the generated answers were consistent with the specifications, demonstrating the LLMs' capability for emotional expression. This indicates the potential for LLM-based AI agents to simulate emotions, opening up a wide range of applications for emotion-based interactions, such as advisors or consultants who can provide advice or opinions with a personal touch.