Language Guided Classifier

Language-guided classifiers aim to improve the accuracy and adaptability of machine learning models by incorporating natural language instructions or explanations to guide the classification process. Current research focuses on enhancing these classifiers' robustness through techniques like disentangling representations from multiple language sources, leveraging multiple "teacher" models for test-time adaptation, and employing data programming to improve performance on novel tasks. This field is significant because it bridges the gap between human-understandable instructions and machine learning capabilities, potentially leading to more flexible, adaptable, and human-centered AI systems across various applications.