Training Set

Training sets, the datasets used to train machine learning models, are a critical focus of current research, with efforts concentrating on improving their quality, size, and diversity. Investigations explore optimal data curation techniques, including filtering, deduplication, and data augmentation, often employing large language models and deep neural networks for analysis and generation of synthetic data. The ultimate goal is to create training sets that lead to more robust, accurate, and generalizable models, impacting various fields from natural language processing and computer vision to biomedical applications and model-based optimization. This research directly addresses challenges like distribution shift and the limitations of existing evaluation methods, ultimately aiming to improve the reliability and performance of machine learning systems.