login
Home / Papers / MetaICL: Learning to Learn In Context

MetaICL: Learning to Learn In Context

DOI: 10.18653/v1/2022.naacl-main.201Source
129 Citations•2022•
Sewon Min, Mike Lewis, Luke Zettlemoyer
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

This work introduces MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of training tasks.

Abstract

Sewon Min, Mike Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.