Labeling Technique

Data labeling techniques are crucial for training machine learning models, particularly in scenarios with limited labeled data. Current research focuses on automating this process using large language models (LLMs) and deep generative models (DGMs) to generate labels from various data sources, including images, text, and sensor data, often employing iterative refinement strategies to improve accuracy. These advancements are improving the efficiency and scalability of machine learning applications across diverse fields, from medical image analysis and venture capital assessment to activity recognition and sentiment analysis. The development of robust and reliable automated labeling methods is vital for accelerating progress in numerous scientific and practical domains.