Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis
This work addresses the problem of reducing labeling costs in supervised learning for NLP practitioners, though it is incremental as it builds on existing AL methods with a novel analysis approach.
The paper tackled the challenge of making active learning (AL) more effective and practical for pre-trained language models by using representation smoothness analysis, resulting in an early stopping technique that improved over random sampling across multiple datasets and AL methods.
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.