Paper Review: Attention Is All You Need — The Transformer Architecture That Changed AI Forever

Introduction Few papers in the history of deep learning have had as profound an impact as “Attention Is All You Need” by Vaswani et al. (2017). Published by researchers at Google Brain and Google Research, this paper introduced the Transformer — an architecture built entirely on attention mechanisms, discarding the recurrent and convolutional layers that had dominated sequence modeling for years. The Transformer didn’t just improve machine translation benchmarks. It became the foundational architecture behind GPT, BERT, T5, Vision Transformers (ViT), and virtually every large language model (LLM) in use today. Understanding this paper is essential for anyone working in modern AI. Paper Info – Title: Attention Is All You Need – Authors: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin – Published: NeurIPS 2017 – Citations: 130,000+ (as of 2025) – Link: arXiv:1706.03762 Motivation and Background The Problem with Recurrent Models Before the Transformer, Recurrent Neural Networks (RNNs) and their variants — LSTMs and GRUs — were the dominant architectures for

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