Self-Attention

Self-attention is the core mechanism of the Transformer architecture, allowing each position in a sequence to attend to every other position when computing its representation. The mechanism computes three projections of the input (queries, keys, values), then for each position takes a weighted sum of all positions' value vectors where the weights are softmax-normalized dot products between the position's query and all keys. Self-attention enables the model to dynamically focus on relevant context regardless of distance, capturing long-range dependencies that recurrent networks struggled with. The mechanism's compute and memory cost is quadratic in sequence length, which historically limited context windows; FlashAttention, sparse attention, and linear attention variants address this scaling. Multi-head attention runs many self-attention operations in parallel with different projection matrices, letting the model attend to different aspects of the input simultaneously. Self-attention is the single most important innovation in the Transformer paper, and every variant of modern LLMs uses some form of self-attention. AI governance teams encounter self-attention as the foundational compute primitive whose costs drive model size, context length, and inference economics.

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