Data Selection

Data selection focuses on optimizing machine learning model training by carefully choosing subsets of available data, aiming to improve model performance, reduce training costs, and enhance efficiency. Current research emphasizes diverse approaches, including rule-based systems leveraging large language models to assess data quality, active learning techniques to iteratively select informative samples, and methods that balance data quality with diversity using clustering or influence scores. These advancements are significant because efficient data selection is crucial for training increasingly large and complex models, particularly in resource-constrained environments and applications requiring high data quality, such as biomedical research and autonomous vehicle development.