Crowdsourcing System

Crowdsourcing systems leverage distributed human intelligence to solve complex tasks, primarily focusing on efficient and accurate data labeling for machine learning. Current research emphasizes improving data quality through advanced models that account for worker heterogeneity and task difficulty, often employing Bayesian methods, spectral clustering, and deep learning architectures like convolutional neural networks and transformers to refine label aggregation and worker skill estimation. These advancements are crucial for various applications, including autonomous driving, natural language processing, and scientific data analysis, by enabling cost-effective and high-quality data generation for training sophisticated algorithms.