MACHINE-LEARNED ARCHITECTURE FOR STRUCTURED SYNTHETIC DATA GENERATION

Original (Chinese)

Techniques may generate realistic synthetic data by programmatically generating a configuration file object type and relationship data. This configuration file may be used to retrieve source data matching the object type(s) and/or specific records indicated by the configuration file. The techniques may detect and anonymize private/proprietary information and may determine statistical characteristic(s) of the source data. A batch of prompt(s) may be generated using the source data, the statistical characteristic(s), and the configuration file and may be transmitted to one or more instances of a transformer-based machine-learned model. Sets of synthetic data received from the model instance(s) may be de-duplicated, checked for similarity to the source data (e.g., via embedding the synthetic data and the source data), and may be used to generate synthetic object(s) using the relationship(s) and/or other data indicated by the configuration file. These synthetic object(s) may then be deployed in a software environment.