Attention is All You Need

This paper introduces the Transformer, a novel neural network architecture based solely on attention mechanisms. Unlike previous sequence-to-sequence models that relied on recurrent or convolutional layers, the Transformer uses self-attention to weigh the importance of different parts of the input sequence, allowing for parallelization and significantly improved performance on machine translation tasks. The core idea is to replace recurrence with attention, enabling faster training and better modeling of long-range dependencies in sequences.