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The Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al. at Google, replaced recurrent neural networks and became the foundation for virtually every modern large language model including GPT, Claude, Gemini, and Llama.

The 'Attention' mechanism, introduced in the paper 'Attention Is All You Need,' is the core innovation of the Transformer architecture. It allows the model to weigh the importance of different words in the input when processing text, which was a major breakthrough for handling long-range dependencies.

Most modern LLMs, including GPT and Gemini, are based on the Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need.' Its key innovation is the attention mechanism.

When processing long documents, try to place key instructions or questions at the beginning or end of the input text. Models sometimes pay more attention to the start/end of context.

The attention mechanism in transformers allows models to focus on relevant parts of the input when generating each token, enabling better understanding of long-range dependencies in text.