Parameter Scaling

Parameter scaling investigates how the performance of machine learning models, particularly large language models, changes with increases in model size (number of parameters) and training data. Current research focuses on understanding and optimizing these scaling relationships, exploring architectures like Mixture-of-Experts (MoE) to improve efficiency and scalability, and developing parameter-efficient fine-tuning techniques to adapt large models to specific tasks without retraining the entire network. These advancements are crucial for building more powerful and resource-efficient AI systems, impacting both fundamental understanding of deep learning and the practical deployment of advanced models across various applications.