Question

What mathematical function is specifically used in Large Language Models to assign different levels of importance to various words in a sequence when calculating dependencies?

Accepted Answer

The mathematical function used is the Softmax function, which acts as the final step within the Scaled Dot-Product Attention mechanism. To determine the importance of words, the model calculates a dot product between a query vector, representing the current word being processed, and a key vector, representing every other word in the sequence. These dot products represent raw alignment scores that indicate how much focus to place on other words. The Softmax function then transforms these raw scores into a probability distribution of values that sum exactly to one. By applying this function, the model ensures that the resulting attention weights are normalized between zero and one, effectively assigning a percentage of relevance to each word. For example, if a sentence contains the words the, cat, and sat, the model assigns high scores to relevant connections, such as linking cat to sat, while the Softmax function ensures these scores are scaled proportionally to represent their relative influence on the final representation.

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What mathematical function is specifically used in Large Language Models to assign different levels of importance to various words in a sequence when calculating dependencies?