How do attention mechanisms improve sequence modeling in neural networks?

Attention lets a neural network look back at every part of the input sequence and decide how much each part matters when predicting the next item. Instead of forcing the model to compress all information into one fixed vector, attention assigns a weight to each token, so the network can focus on the most relevant words. This dynamic weighting makes it easier to capture long‑range relationships and avoids the vanishing‑gradient problem that hurts plain recurrent models. For example, when translating “the cat sat on the mat,” the attention mechanism can give more weight to “cat” when predicting the French word for “cat” while still considering “mat” for context. As a result, sequence modeling becomes more accurate and efficient, especially for long or complex inputs.