"Attention Is All You Need" is a groundbreaking research paper that introduced the Transformer model, a new architecture for natural language processing tasks. Unlike previous models that relied on recurrent neural networks, the Transformer uses a mechanism called self-attention to weigh the importance of different words in a sentence, allowing for better understanding of context and relationships. This model significantly improves the efficiency and effectiveness of training on large datasets. It has become the foundation for many state-of-the-art models, including BERT and GPT, revolutionizing how machines understand and generate human language.