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

Attention lets a sequence model look at every input token and decide how much each one matters for the current output, rather than treating all tokens the same. By computing a weight for each token on the fly, the model can focus on the most relevant words even when they are far apart in the sequence. For instance, when translating “The cat sat on the mat” to Spanish, attention will highlight “cat” and “mat” while down‑weighting filler words, leading to a more accurate translation. This focused selection of useful information is why attention mechanisms improve performance.