A transformer model is a type of deep learning architecture that forms the foundation of modern large language models (LLMs) like GPT, Claude, and Gemini. Introduced by Google researchers in 2017 in the paper “Attention Is All You Need,” transformers changed AI by enabling models to understand context across entire sequences of text at once.
What Makes Transformers Special
Before transformers, AI models read text word by word in order, making it hard to connect context from earlier in a sentence to later parts. Transformers use a mechanism called “self-attention,” which lets the model look at all words in a sequence simultaneously and weigh how much each word relates to every other word.
Why This Matters
Self-attention lets transformers understand nuance, pronoun references, long-range dependencies, and context in ways older models couldn’t. This is why modern AI can generate coherent, context-aware text and answer complex questions.
Examples of Transformer Models
- GPT-4 / GPT-4o: Used in ChatGPT and many AI writing tools
- Claude: Anthropic’s transformer-based assistant
- BERT: Google’s bidirectional transformer, used in Search
- Gemini: Google’s multimodal transformer model
Related: Large Language Model
