For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources, which incurs exceptional pretraining data requirements and a diminished ability to pretrain over small datasets.
<meaning-changed> For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on massive external datasources, which incurs exceptional pretraining data requirements and a diminished ability to pretrain over small datasets.
For natural language processing ' text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on increasingly larger `task-external' data. Transfer learning from high-resource pretraining works well, but research has focused on settings with very large data and compute requirements, while the potential of efficient low-resource learning, without large `task-external' pretraining, remains under-explored. In this work, we evaluate against three core challenges for resource efficient learning. Namely, we analyze: (1) pretraining data requirements and a diminished ability to pretrain over small datasets.