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I'm trying to understand how the self-attention mechanism of the transformer architecture (as proposed by Vaswani et al.) works in detail. I get that self-attention is attention from a token of a sequence to the tokens of the same sequence.

The paper uses the concepts of query, key and value which is aparently derived from retrieval systems. I dont really understand the use of the value. I found this thread, but I don't really get the answer there either.

So let's take an example. Let's say the input sequence is "This forum is awesome". Then to calculate the query vector, I linearly transform the current token (e.g. "This") with a matrix of weights W_Q that are learned during training. In reality, this is apparently bundled in a query matrix $Q$ for every token. I do the same with every token, just with the other matrix $W_K$, where I get the key matrix.

With the scaled dot product I calculate the similarity between my query $\mathrm{embedding}(\text{"This"})\cdot W_Q$ and keys $\mathrm{embedding}(\text{token}) \cdot W_K$ for each token and see which tokens are relevant for "This". (<- is that right?) Now, why do I need to multiply this with the value matrix again, and where does it come from? What's the difference between key and value?

Thanks in advance!

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Because Transformers are black-box models, it is hard to say, what the keys and values really are, but the motivation is that might want to retrieve something else than what is your search criterion.

Imagine something like SQL-like query: get phone numbers of people that have a similar name to "Jindrich". "Jindrich" is a query, the criterion for the search. But you do not want similar names from the database, you want the phone numbers. Phone numbers are the values in this case. The keys are the names already in the phonebook.

The projection for the keys and values in the Transformer model can be understood as extracting a relevant piece of information from the hidden states. E.g., in the Transformer Base architecture, the hidden states are 512-dimensional, but the "extracted" keys and values are only 64-dimensional.

Regarding the multiplication: For simplicity, let's assume we have just one query vector $q$ (and not the full matrix $Q$). First, you compute a similarity score for each of the keys:

$$ \alpha = \mathrm{softmax}(qK/\sqrt{d}) = \mathrm{softmax}\left( \frac{(qk_o, qk_1, \ldots, qk_n)}{\sqrt d} \right) $$

The distribution $\alpha$ is a single-dimensional vector, that only tells you how much each of the keys $k_i \in K$ is relevant for the query $q$. In other words, it says at what positions you should retrieve, but you need something to retrieve and these are the values:

$$\alpha V = \sum_{i=0}^m \alpha_i \cdot v_i $$

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K_V takes the word (or word-part, but let's assume whole words from this discussion) in embedded space, and changes it.

Learning K_V means learning how to change this embedded representation into another embedded representation.

In a sense, what you are asking is "why should we learn how to change the embedding of the words? Why can't we use the original embedding space?"

A fair question.

Consider the following:

  1. After the first layer, we don't have the original words, we have words attended to by the attention mechanism (courtesy of the keys and queries, like you wrote). So we no longer have the word 'This', we have a weird hybrid of, let's say, 0.8*this, 0.15*forum and 0.05*is. We don't want the word that has this value in the original embedded space (if any), we want to make sense of this combination that is relevant to question (i.e. to what we are training this to do).

  2. Please note that we actually have multiple heads of attention, which means that the original value is broken down and is re-concatenated after attention is applied. In order to make use of the information from the different attention heads we need to let the different parts of the value (of the specific word) to effect one another. This is carried out by the Position-wise Feed-Forward layer, but K_V allows more adaptability of the values, which serves their processing in the FF.

  3. While the basic embedding (taking place before the first attention mechanism layer) can be learned specifically to better serve the target question, having learnable W_Vs allows more adaptability to different tasks, as well as allowing you to use the same basic embedding for different tasks (you don't retrain the basic embedding layer when fine-tuning, after all).

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