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We propose a multitask pretraining approach ZeroPrompt for zero-shotgeneralization, focusing on task scaling and zero-shot prompting. Whileprevious models are trained on only a few dozen tasks, we scale to 1,000 tasksfor the first time using real-world data. This leads to a crucial discoverythat task scaling can be an efficient alternative to model scaling; i.e., themodel size has little impact on performance with an extremely large number oftasks. Our results show that task scaling can substantially improve trainingefficiency by 30 times in FLOPs. Moreover, we present a prompting method thatincorporates a genetic algorithm to automatically search for the best promptfor unseen tasks, along with a few other improvements. Empirically, ZeroPromptsubstantially improves both the efficiency and the performance of zero-shotlearning across a variety of academic and production datasets.

ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization