Transformers

Unlike traditional architectures for sequences (such as RNN or LSTM) that processed information element by element, transformers evaluate all elements simultaneously. This allows them to determine the relative importance of each element in relation to all others.

It's as if, instead of reading a text word by word as previous networks did, they could see the entire page simultaneously and understand which words are related to each other, even if they are separated by a lot of text. This ability allows them to better capture the complete context.

Transformers are the foundation of language models such as GPT, Gemini, Claude, and LLaMA. Their impact extends beyond text processing to computer vision, audio analysis, and other fields where capturing complex relationships in data is necessary.

They are currently the dominant architecture for language processing, playing an equivalent role to diffusion models in image generation.