What is Self-Attention in Transformers and how does it compute contextual representations?

Self-attention is a mechanism that allows each token in a sequence to dynamically attend to other tokens to build context-aware representations.

Self-attention is the core innovation behind Transformer architectures and modern LLMs.

Unlike RNNs, which process sequences sequentially, self-attention allows parallel processing while capturing global dependencies.

Core idea: Each token is transformed into three vectors:

Attention computation: Attention(Q, K, V) = softmax(QK^T / √d) V

Step-by-step process:

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