Fine-grained Affective Processing Capabilities Emerging from Large
  Language Models
Joost Broekens Bernhard Hilpert Suzan Verberne Kim Baraka Patrick Gebhard Aske Plaat
Large language models, in particular generative pre-trained transformers
(GPTs), show impressive results on a wide variety of language-related tasks. In
this paper, we explore ChatGPT's zero-shot ability to perform affective
computing tasks using prompting alone. We show that ChatGPT a) performs
meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions,
b) has meaningful emotion representations in terms of emotion categories and
these affective dimensions, and c) can perform basic appraisal-based emotion
elicitation of situations based on a prompt-based computational implementation
of the OCC appraisal model. These findings are highly relevant: First, they
show that the ability to solve complex affect processing tasks emerges from
language-based token prediction trained on extensive data sets. Second, they
show the potential of large language models for simulating, processing and
analyzing human emotions, which has important implications for various
applications such as sentiment analysis, socially interactive agents, and
social robotics.