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（AL）旨在通过查询对模型学习最有益的样本以降低标注成本。尽管AL在微调基于Transformer的预训练语言模型（PLM）中的有效性已得到验证，但通过单一模型获得的AL增益能在多大程度上迁移至其他模型仍不明确。本文探讨文本分类任务中主动获取数据集的迁移性问题，并研究当使用特定PLM通过AL构建的数据集训练不同PLM时，AL增益是否能够持续存在。我们将AL数据集迁移性与不同PLM查询样本的相似性联系起来，证明具有相似获取序列的AL方法（无论采用何种模型）均能产生高度可迁移的数据集。此外，我们表明获取序列的相似性受AL方法选择的影响远大于模型选择。