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  • Formula for Causal Attention

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When calculating the output for the token at position i=5 in a sequence using a causal attention mechanism, the value vector from position j=6 (v_6) is incorporated into the weighted sum.

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Updated 2025-10-04

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  • An auto-regressive model is processing a sequence of 4 tokens. To compute the output for the token at position i=2, it uses a causal attention mechanism. Given the value vectors and the calculated attention weights below, what is the resulting output vector for this position?

    Value Vectors:

    • v_0 = [1.0, 0.0]
    • v_1 = [0.0, 2.0]
    • v_2 = [3.0, 1.0]
    • v_3 = [2.0, 2.0]

    Attention Weights for position i=2:

    • Weight for v_0: 0.1
    • Weight for v_1: 0.3
    • Weight for `
  • Given the formula for the output of a causal attention mechanism for a specific query vector q_i: Att(qi,K≤i,V≤i)=∑j=0iα(i,j)vj\text{Att}(\mathbf{q}_i, \mathbf{K}_{\leq i}, \mathbf{V}_{\leq i}) = \sum_{j=0}^{i} \alpha(i, j) \mathbf{v}_jAtt(qi​,K≤i​,V≤i​)=∑j=0i​α(i,j)vj​ Match each component of the formula to its correct description.

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