What Are Transformer Blocks in LLMs?
At the core of modern large language models (LLMs) such as ChatGPT, Claude, Gemini, and LLaMA is a powerful neural architecture known as the Transformer. Introduced by Vaswani et al. in the 2017 paper Attention Is All You Need , the Transformer architecture fundamentally changed the landscape of natural language processing by enabling models to learn dependencies between words across entire sequences, without relying on recurrence or convolution. A Transformer block is the fundamental building unit of an LLM. It is a modular layer that processes token embeddings through a combination of core components: Multi-head self-attention , which allows the model to focus on relevant parts of the input sequence when interpreting each token. Feed-forward networks (FFNs) , which apply learned transformations to each token representation independently. Residual connections , which help preserve useful information and improve gradient flow during training. ...