Query synthesis methods play a pivotal role in active learning, aiming to generate highly informative queries that are then submitted to human annotators for labeling. Leveraging the existing state of the model and the pool of unlabeled data, these methods strategically craft queries with the goal of significantly enhancing the model's performance. By identifying the most valuable samples from the unlabeled dataset, query synthesis techniques enable more efficient utilization of human annotation efforts, ultimately leading to better-performing machine learning models.
