Meta-training, which fine-tunes the language model (LM)  on various downstream tasks by maximizing the likelihood of the target label given thetask instructionand input instance, has improved the zero-shot task generalization performance. However, meta-trained LMs still struggle to generalize to challenging tasks containing novel labels unseen during meta-training. In this paper, we proposeFlipped Learning, an alternative method of meta-training which trains the LM to generate the task instruction given the input instance and label. During inference, the LM trained withFlipped Learning, referred to asFlipped, selects the label option that is most likely to generate the task instruction. On 14 tasks of the BIG-bench benchmark, the 11B-sizedFlippedoutperforms zero-shot T0-11Band even a 16 times larger 3-shot GPT-3 (175B) on average by 8.4% and 9.7% points, respectively.Flippedgives particularly large improvements on tasks with unseen labels, outperforming T0-11B by up to +20% average F1 score. This indicates that the strong task generalization ofFlippedcomes from improved generalization to novel labels. We release our code atgithub.com/seonghyeonye/Flipped-Learning.