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Pre-trained language models (PLMs) have achieved remarkable success innatural language generation (NLG) tasks. Up to now, most NLG-oriented PLMs arepre-trained in an unsupervised manner using the large-scale general corpus. Inthe meanwhile, an increasing number of models pre-trained with labeled data(i.e. "supervised pre-training") showcase superior performance compared tounsupervised pre-trained models. Motivated by the success of supervisedpre-training, we propose Multi-task superVised Pre-training (MVP) for naturallanguage generation. We collect a large-scale natural language generationcorpus, MVPCorpus, from $77$ datasets over $11$ diverse NLG tasks. Then weunify these examples into a general text-to-text format to pre-train the textgeneration model MVP in a supervised manner. For each task, we furtherpre-train specific soft prompts to stimulate the model's capacity to perform aspecific task. Our MVP model can be seen as a practice that utilizes recentinstruction tuning on relatively small PLMs. Extensive experiments havedemonstrated the effectiveness and generality of our MVP model in a number ofNLG tasks, which achieves state-of-the-art performance on $13$ out of $17$datasets, outperforming BART by $9.3\%$ and Flan-T5 by $5.8\%$.

MVP: Multi-task Supervised Pre-training for Natural Language Generation