What is Transformer?
The transformer is a neural network architecture introduced in the 2017 paper "Attention Is All You Need" that revolutionized natural language processing and became the foundation for modern large language models. Unlike previous sequence models that processed text sequentially, transformers use self-attention mechanisms to process all positions in a sequence in parallel, enabling much more efficient training and better modeling of long-range dependencies. This architectural innovation enabled the scaling that led to modern LLMs.
The transformer architecture consists of encoder and/or decoder blocks built from self-attention layers and feedforward neural networks, with residual connections and layer normalization. The attention mechanism allows each position to attend to all other positions, capturing relationships across the entire sequence. Positional encodings inject sequence order information, and multi-head attention enables the model to capture different types of relationships simultaneously. The parallel processing of positions makes transformers highly efficient on modern hardware.
Transformers power virtually all modern LLMs including GPT, BERT, Claude, and others. Encoder-only transformers (like BERT) excel at understanding tasks, decoder-only transformers (like GPT) excel at generation, and encoder-decoder transformers handle tasks requiring both comprehension and generation. The architecture's success has extended beyond NLP to computer vision, speech processing, and other domains. Understanding transformers is essential for grasping how modern AI systems process and generate language, though using LLMs effectively often doesn't require deep knowledge of the underlying architecture.