Machine Learning (ML) in low-data settings remains an underappreciated yet crucial
problem. Hence, data augmentation methods to increase the sample size of datasets
needed for ML are key to unlocking the transformative potential of ML in data-deprived
regions and domains. Unfortunately, the limited training set constrains traditional tabular
synthetic data generators in their ability to generate a large and diverse augmented dataset
needed for ML tasks. To address this challenge, we introduce CLLM, which leverages the …

Machine Learning (ML) in low-data settings remains an underappreciated yet crucial
problem. This challenge is pronounced in low-to-middle income countries where access to
large datasets is often limited or even absent. Hence, data augmentation methods to
increase the sample size of datasets needed for ML are key to unlocking the transformative
potential of ML in data-deprived regions and domains. Unfortunately, the limited training set
constrains traditional tabular synthetic data generators in their ability to generate a large and …