Active learning (AL) aims to reduce labeling costs by querying the examples most beneficial for model learning. While the effectiveness of AL for fine-tuning transformer-based pre-trained language models (PLMs) has been demonstrated, it is less clear to what extent the AL gains obtained with one model transfer to others. We consider the problem of transferability of actively acquired datasets in text classification and investigate whether AL gains persist when a dataset built using AL coupled with a specific PLM is used to train a different PLM. We link the AL dataset transferability to the similarity of instances queried by the different PLMs and show that AL methods with similar acquisition sequences produce highly transferable datasets regardless of the models used. Additionally, we show that the similarity of acquisition sequences is influenced more by the choice of the AL method than the choice of the model.

翻译：主动学习旨在通过查询对模型学习最有帮助的样本来降低标注成本。尽管主动学习在微调基于Transformer的预训练语言模型（PLMs）方面已被证明有效，但特定模型获得的主动学习增益在多大程度上可迁移至其他模型尚不明晰。本文研究文本分类中主动获取数据集的可迁移性问题，探讨当使用特定PLM结合主动学习方法构建的数据集训练不同PLM时，主动学习增益是否能够保持。我们将主动学习数据集的可迁移性与不同PLM查询实例的相似性相关联，并表明具有相似获取序列的主动学习方法能够产生高度可迁移的数据集，而与所采用的具体模型无关。此外，我们还发现获取序列的相似性受主动学习方法选择的影响大于模型类型选择的影响。