A Comparative Analysis of GPT-3 and BERT Models for Text-based Emotion Recognition: Performance, Efficiency, and Robustness

This paper presents a comparative analysis between the GPT model and a BERT model for emotion recognition. While GPT has shown remarkable performance across various tasks, the question arises as to whether its results can be compared to models specifically trained for particular tasks. The paper explores the presentations of GPT, including its models (Davinci, Curie, Babbage and Ada) as well as the fine-tuning approach. Similarly, the paper discusses the presentation of BERT, specifically DeBERTa v3, and its fine-tuning methodology. A comparison is then drawn between the two models, considering the general vs. task-specific nature of AI. Furthermore, the paper investigates the reasons behind GPT’s exceptional performance and examines why the obtained results for emotion recognition may not be significantly better than those of task-specific models. Ultimately, the conclusion reflects on the better accuracy often exhibited by BERT, but highlights the potential for future advancements, such as the new GPT-4, that may surpass existing models and offer unparalleled versatility in answering various questions.