According to a paper published at the Conference on Language Modeling, synthetic text can also improve the generalization capabilities of language models by providing a variety of text scenarios for the models to learn from. For example, the variations of language an ML model is exposed to in multilingual learning applications by “ensuring a balanced representation of different classes,” thereby combatting representational bias.
Tabular Data
Tabular data, with its unique row-column belarus rcs data organizational structure, is especially beneficial for ML applications in industries where relationships between categorical and numerical variables are crucial, such as finance, healthcare, and retail.
Some use cases include generating synthetic financial transactions to train models for detecting fraudulent activity, creating credit histories to evaluate creditworthiness, developing risk models, and generating synthetic customer data to understand and predict customer behavior.