Attention is All You Need

This paper introduces the Transformer, a novel neural network architecture based solely on attention mechanisms. It avoids recurrence and convolutions entirely. The Transformer model achieves state-of-the-art results in machine translation tasks, demonstrating superior speed and quality compared to previous models relying on recurrent or convolutional layers. The core innovation is the "self-attention" mechanism, which allows the model to weigh the importance of different parts of the input sequence when processing it, leading to better understanding of context and relationships within the data. This architecture has since become a fundamental building block in many areas of AI, including natural language processing.