Synthetic data generation system and method

A system for generating synthetic data, comprising:

• a processor;
• and
• a memory storing instructions that, when executed by the processor, cause the processor to:
• receive user input specifying domain-specific requirements for synthetic data generation and selecting a scenario type, wherein the scenario type is one of Seedless, Seeded, or combination of Seeded and Knowledge Base (KB);
• define a structured schema based on the user input, wherein the structured schema includes data fields, relationships between data fields, and distributional targets;
• and
• based on the structured schema, generate an initial set of synthetic data samples using a neural template-driven generation model trained on domain-specific data,
• apply adversarial contrastive sampling by
• training a discriminator neural network to distinguish between the initial set of synthetic data samples and real data samples,
• using the discriminator neural network to identify generated samples similar to the real data samples, and
• generating a contrastive set of samples dissimilar to those identified as similar by the discriminator, and
• integrate the initial set of synthetic data samples with the contrastive set to create the synthetic data,
• wherein, in response to the scenario type being Seeded or the KB being integrated, the method further comprising:
• performing statistical tests, including Chi-Square or Kolmogorov-Smirnov (KS) tests, to detect divergences between the synthetic data and reference data received for the scenario type being Seeded or the combination;
• and
• upon detection of the divergences, adjusting one or more parameters of the synthetic data creation process, wherein the adjustments include modifying a sampling temperature, increasing template variety, or refining the specificity of a retrieval process to enhance statistical alignment between the synthetic data and the reference data.