Large Language Models (LLM) like ChatGPT have significantly advanced in
recent years and are now being used by the general public. As more people
interact with these systems, improving our understanding of these black box
models is crucial, especially regarding their understanding of human
psychological aspects. In this work, we investigate their emotion perception
through the lens of appraisal and coping theory using the Stress and Coping
Process Questionaire (SCPQ). SCPQ is a validated clinical instrument consisting
of multiple stories that evolve over time and differ in key appraisal variables
such as controllability and changeability. We applied SCPQ to three recent LLMs
from OpenAI, davinci-003, ChatGPT, and GPT-4 and compared the results with
predictions from the appraisal theory and human data. The results show that
LLMs' responses are similar to humans in terms of dynamics of appraisal and
coping, but their responses did not differ along key appraisal dimensions as
predicted by the theory and data. The magnitude of their responses is also
quite different from humans in several variables. We also found that GPTs can
be quite sensitive to instruction and how questions are asked. This work adds
to the growing literature evaluating the psychological aspects of LLMs and
helps enrich our understanding of the current models.

通过评估应激与应对理论，本研究调查了大型语言模型对情绪感知的能力，并与人类数据进行了比较；结果显示了模型在评估和应对的动态方面类似于人类，但在某些维度上与预期不符，且其响应幅度与人类存在较大差异；此外，研究还发现模型对指令和提问方式非常敏感，从而丰富了对当前模型心理学方面的认识。