The Self-Attention mechanism is a central part of the Transformer architecture and plays a crucial role in understanding language in large language models. To explain this mechanism, we use a simplified example:
Imagine you have a sentence: „The dog saw the ball.“ The self-attention mechanism in a transformer model allows each word to be understood in the context of the entire sentence.
This is how the self-attention mechanism works:
- Vectors for each word: First, each word in the sentence is converted into a vector. These vectors are numerical representations of the words learned through training the model.
- Creation of three different vectors: For each word in the sentence, the model creates three different types of vectors – Query, Key and Value. These vectors are created by multiplying the original word vector by three different weight matrices that the model learned during training.
- Calculating attention scores: The mechanism then calculates an attention score for each word with respect to every other word in the sentence. This is done by multiplying the query vector of a word by the key vector of every other word. These scores determine how much attention the model should pay to a word if it understands every other word in the sentence.
- Normalization of scores: The attention scores are normalized so that their sum is one. This is usually done using the softmax function.
- Generating the output vector: Finally, a new vector is calculated for each word by multiplying the normalized attention scores by the value vectors and then summing them. This new vector is a weighted combination of all the words in the sentence, where the weights are the calculated attention scores.
Example:
Returning to our sentence „The dog saw the ball.“ The self-attention mechanism allows the model to understand the meaning of „saw“ in the context of all the other words in the sentence. It would recognize that „saw“ is strongly related to „dog“ and „ball,“ and give this relationship more weight in its processed output for the word „saw.“
In summary, the Self-Attention mechanism enables a Transformer model to understand the context of each word in a sentence by considering the relationships between all the words in the sentence. This leads to a deeper and more accurate understanding of the language.
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