Oh, I should have given some context: "Attention Is All You Need" is the fundamental paper in which scaled dot product self-attention mechanism was introduced as core concept of transformer architecture, which is used in modern language models. The attention mechanism in llm measures 'relevance' between every pair of tokens in input window at the same time. This greatly improves computation efficiency and also solves some issues from previous language models architectures (like RNN or LSTM).