Exploring Active Learning Algorithms for Data Efficient Language Models

Supervised learning is based in the premise that models can effectively solve tasks by learning from numerous examples, mapping inputs to outputs through iterative learning. However, contemporary deep learning models often require vast amounts of labeled data, termed training examples, for optimal performance. Unfortunately, not all training examples contribute equally to the learning process, leading to inefficiencies and resource wastage. Active Learning (AL) has emerged as a powerful paradigm for training language models in a data-efficient manner. By iteratively selecting informative unlabeled data points, which are then annotated by humans to form the training set, AL intelligently guides the training process, optimizing data selection for model improvement over random sampling. This thesis investigates various aspects of active learning algorithms for language mod- els, focusing on model training, data selection, in-context learning and simulation. The thesis is structured along four key publications that tackle these topics respectively. The first publication addresses the effective adaptation of pretrained language models for AL, highlighting the importance of task-specific fine-tuning. The second publication introduces a novel acquisition function, Contrastive Active Learning (CAL), which selects contrastive examples to improve AL performance. The third publication explores active learning principles for in-context learning with large language models, emphasizing the selection of informative demonstrations for few-shot learning. Lastly, the fourth publication critically examines the limitations of simulating AL experiments and pro- poses guidelines for future research. Through these contributions, this thesis aims to advance our understanding of AL algorithms for data-efficient language model training.