Data Scaling

Data scaling, the process of adjusting the size and composition of training datasets, is a critical area of research in machine learning, aiming to optimize model performance and robustness. Current investigations focus on understanding how data scale impacts various model architectures, including large language models (LLMs), graph neural networks, and computer vision models, exploring the interplay between data quantity, diversity, and quality, and the effects of data augmentation and compression techniques. These studies are crucial for improving model efficiency, mitigating issues like overfitting and distribution shift, and enabling the development of more reliable and effective AI systems across diverse applications, from healthcare to natural language processing.