Dataset Size

Dataset size significantly impacts the performance and reliability of machine learning models, particularly in deep learning. Current research focuses on understanding this relationship across various model architectures (including LLMs, VAEs, and CNNs) and tasks, exploring optimal dataset sizes for specific applications and investigating techniques like dataset distillation and continued pretraining to mitigate the computational cost of large datasets. These investigations are crucial for improving model accuracy, efficiency, and robustness, impacting fields ranging from natural language processing and computer vision to medical image analysis and cybersecurity. Furthermore, research addresses the challenges of data imbalance and cross-lingual dataset size comparisons, highlighting the need for more equitable and efficient data practices.