The Good, The Bad, and Why: Unveiling Emotions in Generative AI
It addresses the gap in understanding emotions in generative AI for researchers and developers, offering incremental insights by applying existing psychological concepts to AI models.
This paper tackles the problem of whether generative AI models truly comprehend emotions by incorporating psychological theories, demonstrating that emotional prompts can boost performance by up to 10% on tasks like semantic understanding and logical reasoning, while emotional attacks can hinder it, and revealing that models process emotions similarly to human dopamine mechanisms.
Emotion significantly impacts our daily behaviors and interactions. While recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance AI model performance, 2) EmotionAttack to impair AI model performance, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. Additionally, EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models.