Cross-task learning refers to the approach in machine learning where knowledge gained from one task is utilized to improve performance on a different, but often related, task. This method is beneficial because it allows models to leverage shared information and features across tasks, potentially leading to enhanced generalization and efficiency. By transferring knowledge, models can adapt more quickly and effectively to new challenges.
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