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Attention mechanisms allow sequence-to-sequence models, particularly in tasks like translation or speech recognition, to weigh the importance of different input parts when generating output. This approach helps the model focus on relevant parts of the input, improving its ability to remember and utilize longer input sequences effectively, thereby enhancing overall performance.