<ul><li>The scaling of the dot product between the query and key matrices in transformers is done to prevent the softmax function from being peaky.</li><li>The softmax function is sensitive to the magnitudes of its input, and when large values are supplied, the output becomes peaky.</li><li>Scaling the dot product reduces the variance and stabilizes the training process in neural network architectures.</li><li>The scaled dot product attention mechanism helps in normalizing the attention weights.</li></ul>

Elegant reason for scaling Dot Product between Query and Key Matrices in Transformers

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