The Transformer architecture represents a groundbreaking paradigm shift in natural language processing, as introduced in the seminal paper “Attention is All You Need.” Unlike traditional recurrent neural network (RNN) architectures, Transformers rely on self-attention mechanisms to capture long-range dependencies and relationships within input sequences. This attention mechanism enables Transformers to process input data in parallel, significantly reducing training time and improving performance on a myriad of language tasks.
