Running summarizing tools on a new article

Abstract: The tasks of identifying and classifying ironic texts remain an ongoing challenge, necessitating continued exploration for enhanced solutions in NLP. This study delves into assessing the effectiveness of Generative Pre-trained Transformer (GPT) models, which have emerged in recent years, in handling irony detection and classification tasks through the implementation of zero-shot learning and few-shot learning methods in English texts. Additionally, we compare GPT text embedding models with GloVe, a proven text embedding model, utilizing various machine learning and deep learning approaches. Within this study, we employed the SemEval-2018 Task 3 dataset, curated as part of the Semantic Evaluation 2018 workshop. The most noteworthy achievement in binary classification, namely irony detection, is an F1 score of 68.9%, attained by the text-davinci-003 model through few-shot learning, with access to forty-two samples for training. In terms of multiclass classification, namely irony classification, the text-embedding-ada-002 text embedding model, in conjunction with the Gaussian Naive Bayes algorithm, attained the best result with an F1 score of 48.5%. The best results obtained in the study achieved comparable results with previous studies.