Customizable distillation framework for nl2sql with large language models

A system comprising:

• one or more processors;
• and
• one or more computer-readable media storing instructions which, when executed by the one or more processors, cause the system to perform operations comprising:
• accessing customization information comprising a set of reference natural language queries;
• generating, by one or more first generative artificial intelligence models, a set of synthetic natural language queries based on the customization information and database schema information, wherein each synthetic natural language query of the set of synthetic natural language queries is an augmented version of a reference natural language query of the set of reference natural language queries;
• generating, by one or more second generative artificial intelligence models, a set of distillation training examples based on the set of synthetic natural language queries, wherein generating the set of distillation training examples comprises:
• generating a synthetic programming language query for each synthetic natural language query of the set of synthetic natural language queries, and
• generating each distillation training example of the set of distillation training examples such that each distillation training example comprises:
• (i) a synthetic natural language query, (ii) a database schema from the database schema information associated with the synthetic natural language query, and (iii) a synthetic programming language query corresponding with the synthetic natural language query;
• and
• training a machine learning model with the set of distillation training examples to generate a trained machine learning model.

One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• accessing customization information comprising a set of reference natural language queries;
• generating, by one or more first generative artificial intelligence models, a set of synthetic natural language queries based on the customization information and database schema information, wherein each synthetic natural language query of the set of synthetic natural language queries is an augmented version of a reference natural language query of the set of reference natural language queries;
• generating, by one or more second generative artificial intelligence models, a set of distillation training examples based on the set of synthetic natural language queries, wherein generating the set of distillation training examples comprises:
• generating a synthetic programming language query for each synthetic natural language query of the set of synthetic natural language queries, and
• generating each distillation training example of the set of distillation training examples such that each distillation training example comprises:
• (i) a synthetic natural language query, (ii) a database schema from the database schema information associated with the synthetic natural language query, and (iii) a synthetic programming language query corresponding with the synthetic natural language query;
• and
• training a machine learning model with the set of distillation training examples to generate a trained machine learning model.