  Sarcasm is a form of irony that requires readers or listeners to interpret
its intended meaning by considering context and social cues. Machine learning
classification models have long had difficulty detecting sarcasm due to its
social complexity and contradictory nature.
  This paper explores the applications of the Generative Pretrained Transformer
(GPT) models, including GPT-3, InstructGPT, GPT-3.5, and GPT-4, in detecting
sarcasm in natural language. It tests fine-tuned and zero-shot models of
different sizes and releases.
  The GPT models were tested on the political and balanced (pol-bal) portion of
the popular Self-Annotated Reddit Corpus (SARC 2.0) sarcasm dataset. In the
fine-tuning case, the largest fine-tuned GPT-3 model achieves accuracy and
$F_1$-score of 0.81, outperforming prior models. In the zero-shot case, one of
GPT-4 models yields an accuracy of 0.70 and $F_1$-score of 0.75. Other models
score lower. Additionally, a model's performance may improve or deteriorate
with each release, highlighting the need to reassess performance after each
release.
