How do attention mechanisms improve sequence modeling in neural networks?

Attention lets a neural network focus on the most relevant parts of a sequence when making a prediction, instead of treating all positions equally. By assigning a weight to each token, the model can decide how much information to draw from each word or time step, which improves the representation of context. The attention scores are computed from the query, key, and value vectors, and the weighted sum of values gives a context‑aware output. For example, when translating “I love you” to Spanish, the model can give higher weight to “love” when predicting the verb “amar”, ignoring less important words. This selective focus makes the network more flexible and accurate for tasks like translation, summarization, and speech recognition.