In the context of attention mechanisms, how do they improve model performance in sequence tasks?

Attention lets a model look at every part of a sequence and decide how much each part matters for the current output, so it can pick the right words even when they are far apart. This reduces the need for the model to remember long histories in its hidden state, making learning faster and more accurate. It also allows the whole sequence to be processed in parallel, speeding up training. For example, when translating “I ate the apple” to Spanish, the attention layer can match “ate” with “comí” and “apple” with “manzana” directly, instead of relying on a chain of hidden states. The result is higher accuracy and faster inference for tasks like translation, summarization, or speech recognition.