Emotion Generation

Emotion generation research focuses on enabling computers to create outputs (text, images, etc.) that evoke specific emotions in users, mirroring human emotional expression and understanding. Current efforts leverage large language models (LLMs) and diffusion models, often employing techniques like prompt engineering, chain-of-thought prompting, and fine-tuning to improve emotional fidelity and diversity in generated content. This field is significant for advancing affective computing, with applications ranging from improving human-computer interaction and mental health support to mitigating the spread of misinformation and enhancing creative design tools.