This article discusses the use of variational autoencoders (VAEs) to generate synthetic data as a solution to the impending data scarcity for training large language models. It explores how synthetic data can address issues like imbalanced datasets, particularly using the UCI Adult dataset, by generating synthetic samples to balance the dataset and improve classification accuracy.
