  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.
