An Extensive Analysis Between Different Language Models: GPT-3, BERT and MACAW

Abstract The increase in human interactions with commercial applications has given rise to the demand for Interactive interfaces like chat- bots, text translators, text predictors, and text generators which use pre-trained Language Models to perform their own specific tasks. Language models are leading-edge technologies that enable machines to read, decode, comprehend, and make sense of human languages and respond in appropriate ways. In this paper GPT-3, BERT and Macaw language models are tested on different categorial questions to understand their architecture and behaviour in various circumstances. GPT-3 being pre-trained on a robust dataset gives very elaborate and human like answers, while the outputs produced by BERT can be customised by providing custom context and on the other hand Macaw shows more accuracy while answering to general questions.