The Transformer architecture, introduced in the paper "Attention Is All You Need" by Vaswani et al., is a breakthrough in neural network design that significantly improves the ability of models to process sequences of data. It allows for parallel processing of sequences and has been foundational in the development of [[Large Language Model (LLM)]]. This architecture uses mechanisms like self-attention and position-wise feedforward networks, enabling models to efficiently handle long-range dependencies in text.