Attention Is All You Need
The paper introduced the Transformer, a sequence-transduction architecture based entirely on attention mechanisms, dispensing with the recurrence and convolutions used by prior state-of-the-art models. By relying on multi-head self-attention, the model is more parallelizable and trains substantially faster, while achieving new state-of-the-art results on machine translation. The architecture became the foundation for subsequent large language models and much of modern deep learning.