Transformer
A transformer is the neural network architecture that powers virtually all modern large language models, including GPT, Claude, Gemini, and LLaMA. Introduced in the 2017 paper "Attention Is All You Need," transformers use self-attention mechanisms to process all input tokens in parallel rather than sequentially, enabling efficient training on massive datasets and strong performance on language tasks.
Example
Before transformers, recurrent neural networks (RNNs) processed text one word at a time, struggling with long documents. The transformer architecture processes the entire input simultaneously — a 10,000-word document is analyzed in one pass, with attention connecting every word to every other word, which is why modern models can understand context across entire essays.
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