Advancements in Natural language Processing: An In-depth Review of Language Transformer Models

Abstract: This review paper provides a succinct exploration of the history and impact of language transformer models in natural language processing (NLP). Beginning with a literature review, we trace the evolution of key models and elucidate fundamental concepts like self-attention mechanism and positional encoding. A comparative analysis of major transformer models, including BERT, GPT, T5, XLNet, offers insights into their architectures, strengths, and weaknesses. The discussion extends to pretraining objectives, fine-tuning strategies, and evaluation metrics, complemented by real-world examples of successful applications in NLP. We address current challenges, discuss potential future directions, and explore ethical considerations, providing valuable suggestions for researchers and practitioners in the NLP community in our conclusive summary.