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Incorporating Positional Bias into Attention Scores
The standard formula for a scaled dot-product attention score between a query vector q_i and a key vector k_j is (q_i ⋅ k_j) / sqrt(d). How would you modify this formula to include a relative positional bias term, PE(i, j), that is added directly to this score before the normalization step?
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Ch.2 Generative Models - Foundations of Large Language Models
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A self-attention model incorporates positional awareness by adding a bias term directly to the query-key dot product for each pair of positions
(i, j). This bias term's value depends on the relative distance betweeniandj. What is the primary implication of this approach compared to the alternative of adding positional vectors to the input token embeddings?Incorporating Positional Bias into Attention Scores
In a self-attention mechanism, the score computed between a query at position
iand a key at positionjis modified by directly adding a bias term whose value depends only on the positionsiandj. What is the primary function of this bias term within the attention calculation?