Few-shot learning involves learning an effective model from only a few labeled datapoints. The use of a small training set makes it difficult to avoid overfitting but also makes few-shot learning applicable to many important real-world settings. In this work, we focus on Few-shot Learning with Auxiliary Data (FLAD), a training paradigm that assumes access to auxiliary data during few-shot learning in hopes of improving generalization. Introducing auxiliary data during few-shot learning leads to essential design choices where hand-designed heuristics can lead to sub-optimal performance. In this work, we focus on automated sampling strategies for FLAD and relate them to the explore-exploit dilemma that is central in multi-armed bandit settings. Based on this connection we propose two algorithms -- EXP3-FLAD and UCB1-FLAD -- and compare them with methods that either explore or exploit, finding that the combination of exploration and exploitation is crucial. Using our proposed algorithms to train T5 yields a 9% absolute improvement over the explicitly multi-task pre-trained T0 model across 11 datasets.

翻译：少样本学习旨在仅通过少量标注数据点学习有效模型。小规模训练集的使用不仅难以避免过拟合，还使少样本学习可应用于众多重要实际场景。本研究聚焦于带辅助数据的少样本学习（FLAD），该训练范式假设在少样本学习过程中可获取辅助数据，以期提升泛化性能。在少样本学习中引入辅助数据会产生关键的设计选择，手动设计的启发式方法可能导致次优性能。为此，我们重点研究FLAD的自动采样策略，并将其与多臂赌博机场景的核心问题——探索-利用困境相联系。基于此关联，我们提出两种算法——EXP3-FLAD和UCB1-FLAD，并与纯探索或纯利用的方法进行比较，发现探索与利用的结合至关重要。采用我们提出的算法训练T5模型，在11个数据集上相比显式多任务预训练的T0模型实现了9%的绝对性能提升。