Transformer-based Language Models - Architectures and Applications: Analyzing transformer-based language models such as BERT, GPT, and T5, and their applications in NLP tasks such as text generation and classification

Abstract

Transformer-based language models have revolutionized natural language processing (NLP) by enabling efficient training on large-scale datasets and achieving state-of-the-art performance on various tasks. This paper provides an in-depth analysis of transformer-based language models, focusing on key architectures like BERT, GPT, and T5. We explore the underlying mechanisms of transformers, including self-attention and positional encoding, and discuss how these models have been applied to NLP tasks such as text generation and classification. Additionally, we examine the strengths and limitations of transformer-based models and discuss future research directions in this field.