Google’s introduction to large language models explains how transformers and self-attention support modern LLMs.
How does transformer architecture work?
After tokenization, the model converts token identifiers into vectors and processes them through repeated attention and feed-forward layers. Attention assigns different weight to other positions when building each token’s representation, helping the model connect instructions, references, and evidence across the input. Positional information preserves order because attention alone does not encode sequence.
Why are transformers useful for language models?
Their parallel training made it practical to learn from very large datasets, while scaling model size and data improved broad language capabilities. A larger context window lets a transformer consider more input, but does not guarantee it will use every detail reliably. The architecture enables contextual prediction, not factual verification. Production systems still need retrieval, tools, and evaluation around an LLM, while embeddings reuse learned representations for search and matching.