Question

Which specific Transformer component enables the model to process words in parallel by calculating the relationship between every word in a sequence simultaneously?

Accepted Answer

The specific component that enables parallel processing is the self-attention mechanism. Unlike older models that processed words one by one in a fixed order, self-attention looks at the entire sequence of words simultaneously. It calculates the relationship between every word in a sequence by assigning a weight to each pair of words, which represents how much focus one word should place on another to understand the context. For example, in the sentence the animal didn&#x27;t cross the street because it was too tired, the self-attention mechanism allows the model to connect the word it to the word animal by calculating their relationship score across the entire input at once. This mechanism uses three vectors for each word called Query, Key, and Value. The Query represents the current word looking for information, the Key acts as a label to match against other words, and the Value contains the actual content of the word. By performing matrix multiplications on these vectors for all words in the input simultaneously, the model can determine the relevance of every word to every other word without needing to wait for a previous word to be processed first.

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Which specific Transformer component enables the model to process words in parallel by calculating the relationship between every word in a sequence simultaneously?