• Shibaditya Deb

This review paper examines the Transformer architecture, a groundbreaking innovation in natural language processing (NLP) that has revolutionized large language models. Introduced by Vaswani et al. in 2017, the Transformer employs self-attention mechanisms to process sequential data efficiently, eliminating the need for recurrent neural networks. The paper explores the core components of the Transformer, including multi-head attention, positional encoding, and feed-forward networks. It highlights the architecture's advantages, such as parallel processing capabilities and improved long-range dependency modeling. The impact of Transformer-based models on various NLP tasks is discussed, emphasizing their superior performance in machine translation, text summarization, and question answering. The review also addresses the scalability of Transformer models, leading to the development of increasingly large and powerful language models. Finally, the paper outlines future research directions, including efforts to enhance model efficiency, interpretability, and ethical considerations in deploying large language models. This comprehensive review provides researchers and practitioners with a thorough understanding of the Transformer architecture's significance in advancing NLP technologies.

Transformer,Large Language Model,Nerual Language Processing,Artificial Intelligence

"Transformers in Large Language Models: Foundations, Advances, and Future Directions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.a548-a559, July-2025, Available :http://www.jetir.org/papers/JETIR2507060.pdf

"Transformers in Large Language Models: Foundations, Advances, and Future Directions", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppa548-a559, July-2025, Available at : http://www.jetir.org/papers/JETIR2507060.pdf