Attention Is All You Need: A Technical Review of the Transformer Architecture and Its Impact on Modern Artificial Intelligence

Abstract

This technical review examines the Transformer architecture introduced in “Attention Is All You Need,” with emphasis on self-attention, scaled dot-product attention, multi-head attention, positional encoding, and the encoder-decoder architecture. It analyzes how the Transformer addressed computational limitations of recurrent sequence models and reviews the experimental evidence presented in the original work. The paper further examines the architectural influence of the Transformer on subsequent developments including GPT, BERT, Transformer-XL, Reformer, Longformer, GPT-3, and Vision Transformer. Limitations involving quadratic attention complexity, long-context processing, computational requirements, and interpretability are also discussed, together with directions for future research.