CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
This addresses the challenge of building efficient few-shot learners for diverse NLP tasks, though it is incremental as it builds on existing few-shot learning frameworks.
The paper tackles the problem of cross-task generalization in few-shot NLP learning by introducing CrossFit, a standardized setup, and the NLP Few-shot Gym with 160 tasks, showing that upstream learning on seen tasks improves few-shot performance on unseen tasks, with task selection significantly influencing results.
Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge obtained when learning prior tasks. In this paper, we explore whether and how such cross-task generalization ability can be acquired, and further applied to build better few-shot learners across diverse NLP tasks. We introduce CrossFit, a problem setup for studying cross-task generalization ability, which standardizes seen/unseen task partitions, data access during different learning stages, and the evaluation protocols. To instantiate different seen/unseen task partitions in CrossFit and facilitate in-depth analysis, we present the NLP Few-shot Gym, a repository of 160 diverse few-shot NLP tasks created from open-access NLP datasets and converted to a unified text-to-text format. Our analysis reveals that the few-shot learning ability on unseen tasks can be improved via an upstream learning stage using a set of seen tasks. We also observe that the selection of upstream learning tasks can significantly influence few-shot performance on unseen tasks, asking further analysis on task similarity and transferability.