Unsupervised Cross-Task Generalization via Retrieval Augmentation

• URL: http://arxiv.org/abs/2204.07937v1
• Date: Sun, 17 Apr 2022 06:05:13 GMT
• Title: Unsupervised Cross-Task Generalization via Retrieval Augmentation
• Authors: Bill Yuchen Lin, Kangmin Tan, Chris Miller, Beiwen Tian, Xiang Ren
• Abstract summary: We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data. Our empirical results show that the proposed ReCross consistently outperforms non-retrieval baselines by a significant margin.
• Score: 27.47782160720298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans can perform unseen tasks by recalling relevant skills that are acquired previously and then generalizing them to the target tasks, even if there is no supervision at all. In this paper, we aim to improve such cross-task generalization ability of massive multi-task language models such as T0 (Sanh et al., 2021) in an unsupervised setting. We propose a retrieval-augmentation method named ReCross that takes a few unlabelled examples as queries to retrieve a small subset of upstream data and uses them to update the multi-task model for better generalization. Our empirical results show that the proposed ReCross consistently outperforms non-retrieval baselines by a significant margin.

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