Active learning is a research area in the field of machine learning that focuses on improving the learning process by allowing the algorithm to interactively query an oracle (usually a human annotator) for labels on specific examples. This approach aims to select the most informative examples for the algorithm to learn from, thereby reducing the amount of labeled data needed to achieve a desired level of performance. Active learning methods have applications in various domains, including natural language processing, computer vision, and healthcare. The goal of active learning research is to develop efficient strategies for selecting informative examples to improve the learning process and reduce the annotation cost.