Paper Title
Examining the Performance of NLP Language Models: A Comparison Study

The growing usage of commercial applications has led to an increase in human-machine interactions. As a result, there is a high demand for interactive interfaces such as chat bots, text translators, text predictors, and text generators that utilize pre-trained language models to perform specific tasks. Language models are advanced technologies that enable machines to read, understand, interpret, and respond appropriately to human languages. This research paper aims to compare the performance of three popular language models, namely GPT-3, BERT, and Macaw, in answering different categorical questions to understand their architecture and behavior in various contexts. This paper provides a basic overview of the three mentioned NLP models and gives a detailed performance analysis between them. Keywords - Language Models, Natural Language Processing, Artificial Intelligence, Text Analysis, Interactive Interfaces