CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP

Qinyuan Ye, Bill Yuchen Lin, Xiang Ren


Abstract
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.
Anthology ID:
2021.emnlp-main.572
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7163–7189
Language:
URL:
https://aclanthology.org/2021.emnlp-main.572
DOI:
10.18653/v1/2021.emnlp-main.572
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.572.pdf
Software:
 2021.emnlp-main.572.Software.rar
Code
 INK-USC/CrossFit +  additional community code
Data
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