Attention

Attention mechanisms are computational strategies that enable models to focus on specific parts of the input data when generating outputs, effectively allowing for context-aware processing. The fundamental operation involves computing a weighted sum of values (V) based on the relevance of queries (Q) to keys (K), expressed mathematically as Attention(Q, K, V) = softmax(QK^T / √d_k)V. This mechanism allows for dynamic weighting of input features, facilitating the capture of long-range dependencies and contextual relationships within sequences. Attention mechanisms can be categorized into various forms, including global attention, where all input tokens are considered, and local attention, which restricts the focus to a subset of tokens. The introduction of attention has significantly improved the performance of models in tasks such as machine translation and text summarization, leading to the development of architectures like Transformers that leverage attention as a core component.