Human Labeled

Human-labeled data remains crucial for training and evaluating machine learning models, particularly in natural language processing, despite the rise of large language models (LLMs). Current research focuses on mitigating the high cost and time associated with human labeling through techniques like automated data generation using LLMs, active learning to prioritize labeling of informative data points, and prediction-powered inference to leverage both human and automatically generated labels. These advancements aim to improve the efficiency and scalability of creating high-quality datasets for various tasks, ultimately enhancing the performance and reliability of machine learning systems across diverse scientific and practical applications.