Understanding Self-Attention: The Core Mechanism Behind Transformers

Self-attention has become a key mechanism in artificial intelligence nowadays, driving some of the most sophisticated models in natural language processing (NLP) and other fields. Let's examine self-attention's foundations, advantages, and influence on machine learning to see why it is so revolutionary.

What is Self-Attention?

One of the main mechanisms in the Transformer architecture that enables a model to assess the relative relevance of various parts in a sequence is self-attention. Self-attention processes every element simultaneously, as opposed to standard models that process input sequentially. This improves efficiency and accurately reflects long-range dependencies.

 Self-attention is flexible enough to handle a wide range of data formats because it can concentrate on pertinent portions of the input by creating query, key, and value vectors from each token and calculating attention scores. This capacity has transformed domains such as computer vision and natural language processing, propelling progress in models like BERT and GPT.

How Does Attention to Oneself Operate?
In summary, each input token is used to create three vectors—a query vector, a key vector, and a value vector—that are then used by self-attention. Following that, these vectors are utilised to calculate attention scores, which establish the relative emphasis that each token in the sequence should acquire from the other tokens.

Query, Key, and Value Vectors: Using acquired linear transformations, every character in the input sequence is converted into one of these three vectors.

Attention Scores: To determine an attention score, take the dot product of a token's query vector and all of the tokens' key vectors. How much weight a token should provide to other tokens is indicated by this score.

Scaling: The scores are scaled by the square root of the dimension of the key vectors in order to keep the dot product from getting too big, which could compromise the stability of the gradients during training.

Softmax: To normalise the scaled scores into a probability distribution, they are run via a softmax function.

Weighted Sum: The final output representation for each token is obtained by adding the weights assigned to each value vector by these normalised scores.