Understanding Attention Mechanisms - Turning Similarity Scores into Attention Weights
This article explores how to use dot product to calculate similarity scores between input words and the end-of-sequence (EOS) token, and then apply the softmax function to turn these scores into attention weights.
Why it matters
Attention mechanisms are a core component of many state-of-the-art neural network models, so understanding how they work is crucial for developing advanced AI systems.
Key Points
- 1Dot product is used to calculate similarity scores between input words and EOS token
- 2Higher similarity scores indicate the input word should have more influence on the first decoded word
- 3Softmax function is applied to the similarity scores to convert them into attention weights between 0 and 1
- 4Attention weights determine the percentage of each encoded input word to use when decoding
Details
The article builds on the previous part by explaining how to use dot product to calculate similarity scores between input words and the EOS token. These scores indicate how much each input word should influence the first decoded output. To convert the raw scores into attention weights, the softmax function is applied, which normalizes the values between 0 and 1 and ensures they sum to 1. This allows the decoder to use a weighted combination of the encoded input words, with more influential words having a higher attention weight. The author notes this is an important step in understanding how attention mechanisms work in neural networks.
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