Transformer Architecture

The Transformer Architecture is the foundational neural network design that revolutionized natural language processing and enabled the creation of modern large language models. Introduced in the landmark 2017 paper "Attention Is All You Need" by Google researchers, it replaced older recurrent architectures (RNNs, LSTMs) with a parallelizable mechanism based entirely on attention.

Key innovations of the Transformer include:

• Self-Attention Mechanism: Allows the model to weigh the importance of different words in a sequence relative to each other, enabling it to capture long-range dependencies.
• Parallel Processing: Unlike sequential models, Transformers process entire sequences simultaneously, dramatically reducing training time.
• Positional Encoding: Injects information about word order into the model, since self-attention itself is position-agnostic.
• Encoder-Decoder Structure: The original design featured both encoding (understanding input) and decoding (generating output) components, though many modern LLMs use decoder-only architectures.

This architecture is the backbone of virtually every major AI model today, including GPT, Claude, Gemini, and LLaMA. Understanding Transformers is essential for anyone working seriously with AI systems.