Could you explain the concept of an attention model?

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Anonymous · Accepted Answer

An attention model is an example of a hyper network where the weights of model
are determined by the input itself. In the attention mechanism, this occurs in
that each token of the input sequence is compared with all others in the context
window to determine the next token. The Attention mechanism by default does not
care about the order of the input, which is ironic because of the success it has
found in next token prediction. This is the basis for the LLMs. The prompt
(which may be modified on the backend) will be used and then the next token will
be predicted for the answer, and then the answer is built up token by token.

Could you explain the concept of an attention model?

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