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Leveraging task-aware annotated data as supervised signals to assist withself-supervised learning on large-scale unlabeled data has become a new trendin pre-training language models. Existing studies show that multi-task learningwith large-scale supervised tasks suffers from negative effects across tasks.To tackle the challenge, we propose a task prefix guided multi-taskpre-training framework to explore the relationships among tasks. We conductextensive experiments on 40 datasets, which show that our model can not onlyserve as the strong foundation backbone for a wide range of tasks but also befeasible as a probing tool for analyzing task relationships. The taskrelationships reflected by the prefixes align transfer learning performancebetween tasks. They also suggest directions for data augmentation withcomplementary tasks, which help our model achieve human-parity results oncommonsense reasoning leaderboards. Code is available athttps://github.com/cooelf/CompassMTL

Task Compass: Scaling Multi-task Pre-training with Task Prefix