What's Next in Affective Modeling? Large Language Models
Nutchanon Yongsatianchot Tobias Thejll-Madsen Stacy Marsella
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.