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Deep active learning (DAL) seeks to reduce annotation costs by enabling themodel to actively query instance annotations from which it expects to learn themost. Despite extensive research, there is currently no standardized evaluationprotocol for transformer-based language models in the field of DAL. Diverseexperimental settings lead to difficulties in comparing research and derivingrecommendations for practitioners. To tackle this challenge, we propose theActiveGLAE benchmark, a comprehensive collection of data sets and evaluationguidelines for assessing DAL. Our benchmark aims to facilitate and streamlinethe evaluation process of novel DAL strategies. Additionally, we provide anextensive overview of current practice in DAL with transformer-based languagemodels. We identify three key challenges - data set selection, model training,and DAL settings - that pose difficulties in comparing query strategies. Weestablish baseline results through an extensive set of experiments as areference point for evaluating future work. Based on our findings, we provideguidelines for researchers and practitioners.

ActiveGLAE: A Benchmark for Deep Active Learning with Transformers