The Emotional Intelligence of the GPT-4 Large Language Model

Abstract

Background. Advanced AI models such as the large language model GPT-4 demonstrate sophisticated intellectual capabilities, sometimes exceeding human intellectual performance. However, the emotional competency of these models, along with their underlying mechanisms, has not been sufficiently evaluated. Objective. Our research aimed to explore different emotional intelligence domains in GPT-4 according to the Mayer–Salovey–Caruso model. We also tried to find out whether GPT-4's answer accuracy is consistent with its explanation of the answer. Design. The Russian version of the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) sections was used in this research, with questions asked as text prompts in separate, independent ChatGPT chats three times each. Results. High scores were achieved by the GPT-4 Large Language Model on the Understanding Emotions scale (with scores of 117, 124, and 128 across the three runs) and the Strategic Emotional Intelligence scale (with scores of 118, 121, and 122). Average scores were obtained on the Managing Emotions scale (103, 108, and 110 points). However, the Using Emotions to Facilitate Thought scale yielded low and less reliable scores (85, 86, and 88 points). Four types of explanations for the answer choices were identified: Meaningless sentences; Relation declaration; Implicit logic; and Explicit logic. Correct answers were accompanied by all types of explanations, whereas incorrect answers were only followed by Meaningless sentences or Explicit logic. This distribution aligns with observed patterns in children when they explore and elucidate mental states. Conclusion. GPT-4 is capable of emotion identification and managing emotions, but it lacks deep reflexive analysis of emotional experience and the motivational aspect of emotions.

Keywords

• artificial empathy
• artificial psychology
• chatgpt
• emotional intelligence (ei)
• emotional quotient (eq)
• gpt-4
• machine behavior