Transformer Architecture

The Transformer Architecture is a Deep Learning model structure introduced in the 2017 paper “Attention Is All You Need” by researchers at Google. It processes sequential data through Attention Mechanisms rather than the recurrence used by Recurrent Neural Networks, including Long Short-Term Memory networks. Its Self-Attention mechanism supports modeling Long-Range Dependencies and enables greater Parallelization during training than recurrent models, while Positional Encoding supplies information about sequence order. In Autoregressive Generation, decoder self-attention uses Causal Masking to prevent attention to future tokens. The original architecture consists of Encoder and Decoder stacks incorporating Multi-Head Attention and Feed-Forward Neural Networks; subsequent variants also include encoder-only and decoder-only designs. Its Scalability and adaptability across tasks and hardware platforms support its widespread use as a backbone for Foundation Models and Large Language Models, including the GPT series by OpenAI, Claude by Anthropic, and Gemini by Google DeepMind.