Is GPT a Computational Model of Emotion? Detailed Analysis
It addresses the problem of understanding and improving emotional AI capabilities for researchers and developers, though it is incremental in assessing existing models.
This paper investigated GPT's emotional reasoning abilities by analyzing autobiographical memories and varying situational aspects, finding that GPT's predictions align significantly with human appraisals without prompt engineering, but it struggles with predicting emotion intensity and coping responses.
This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering, it is shown that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses. GPT-4 showed the highest performance in the initial study but fell short in the second, despite providing superior results after minor prompt engineering. This assessment brings up questions on how to effectively employ the strong points and address the weak areas of these models, particularly concerning response variability. These studies underscore the merits of evaluating models from a componential perspective.