The attention mechanism is a computational technique in neural networks that allows a model to weight dynamically the importance of different parts of the input data when producing its output. Unlike older approaches, which processed the input sequence into a single fixed-length context vector, the attention mechanism re-evaluates the whole input sequence at every step of generating the output. For each input element it computes an attention score determining its relevance to the current context. The process is often modelled using queries, keys and values, where the model compares the query (the current context) with the keys (all the input elements) in order to determine the weights for combining the values.
Imagine reading a cooking recipe and wanting to find out how much sugar you need. Rather than memorising the whole recipe from beginning to end, your eyes naturally hunt for the word “sugar” and you focus on the information about its quantity. The attention mechanism in artificial intelligence works very similarly. It lets the model “look” at all the available data while solving a task and decide for itself which parts matter most at that moment. Just as you highlight the key ingredient in a recipe, the model assigns the highest “attention” to the most important words or data and then uses them to construct the correct answer.