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

Attention lets a model look at all parts of the input sequence at once instead of only the last hidden state, so it can give each word a weight that shows how important it is for the current prediction. By computing a weighted sum of all hidden states, the model can focus on the most relevant words, which improves accuracy on tasks like translation or summarization. This reduces the problem of long‑range dependencies that recurrent networks struggle with, because the attention scores can connect distant words directly. For example, when translating “I love Paris,” the model can give the word “Paris” a high weight when predicting the French word “Paris,” even if the words are far apart in the sentence. The result is faster, more accurate predictions and easier parallel computation.