Developed to alleviate prohibitive labeling costs, active learning (AL) methods aim to reduce label complexity in supervised learning. While recent work has demonstrated the benefit of using AL in combination with large pre-trained language models (PLMs), it has often overlooked the practical challenges that hinder the effectiveness of AL. We address these challenges by leveraging representation smoothness analysis to ensure AL is feasible, that is, both effective and practicable. Firstly, we propose an early stopping technique that does not require a validation set -- often unavailable in realistic AL conditions -- and observe significant improvements over random sampling across multiple datasets and AL methods. Further, we find that task adaptation improves AL, whereas standard short fine-tuning in AL does not provide improvements over random sampling. Our work demonstrates the usefulness of representation smoothness analysis for AL and introduces an AL stopping criterion that reduces label complexity.

翻译：为缓解监督学习中高昂的标注成本，主动学习方法致力于降低标签复杂度。尽管近期研究已证实主动学习与大型预训练语言模型结合使用的优势，但现有工作往往忽略了阻碍主动学习有效性的实际挑战。我们通过引入表示平滑性分析来应对这些挑战，以确保主动学习既可行又实用。首先，我们提出了一种无需验证集（实际主动学习场景中通常无法获取）的早停技术，并在多个数据集和主动学习方法上观察到相比随机采样的显著改进。进一步发现，任务适配能提升主动学习效果，而标准短微调在主动学习中相比随机采样并无优势。本研究证明了表示平滑性分析对主动学习的价值，并引入了一种能降低标签复杂度的主动学习停止准则。