What's Next in Affective Modeling? Large Language Models
This work addresses affective modeling for psychology and AI researchers, but it is incremental as it applies an existing method to a new domain.
The paper explores GPT-4's ability to perform emotion-related tasks, such as predicting emotions, distinguishing theories, generating stories, and manipulating emotional intensity, showing it can generally make correct inferences.
Large Language Models (LLM) have recently been shown to perform well at various tasks from language understanding, reasoning, storytelling, and information search to theory of mind. In an extension of this work, we explore the ability of GPT-4 to solve tasks related to emotion prediction. GPT-4 performs well across multiple emotion tasks; it can distinguish emotion theories and come up with emotional stories. We show that by prompting GPT-4 to identify key factors of an emotional experience, it is able to manipulate the emotional intensity of its own stories. Furthermore, we explore GPT-4's ability on reverse appraisals by asking it to predict either the goal, belief, or emotion of a person using the other two. In general, GPT-4 can make the correct inferences. We suggest that LLMs could play an important role in affective modeling; however, they will not fully replace works that attempt to model the mechanisms underlying emotion-related processes.