What is the primary reason attention mechanisms improve the performance of sequence models in capturing relevant information?

Attention mechanisms let a sequence model decide which parts of the input to focus on at each step, so it can give more weight to the most relevant tokens. By computing a weighted sum of all input positions, the model can dynamically highlight important words while down‑weighting irrelevant ones. This selective focus preserves long‑range dependencies that would otherwise be lost in a fixed‑size context window. For example, when translating “the cat sat on the mat,” the attention layer can give a high weight to “cat” when predicting “sat,” ignoring filler words. As a result, the model captures the right context and improves overall performance.