Dynamic Vocabularies for Accurate Natural-Language-to-SQL Generation

The patent segments the large fixed vocabulary into multiple specialized vocabularies organized in a hierarchy. Each vocabulary corresponds to specific SQL constructs (e.g., SELECT, JOIN, WHERE) or database concepts. This segmentation allows the model to select appropriate vocabularies for different transformation tasks, improving both coverage and accuracy by avoiding the noise of irrelevant vocabulary terms.

The patent implements dynamic vocabulary selection where the appropriate vocabulary is chosen based on the input natural language query characteristics. The system dynamically adapts which vocabulary to use during inference, rather than relying on a static fixed vocabulary. This dynamic approach enables the model to adjust its vocabulary usage to match the specific SQL transformation task at hand, reducing hallucinations while maintaining versatility.

The patent creates a universal vocabulary framework that serves multiple functions: it supports various SQL dialects, handles different database schemas, and adapts to diverse natural language inputs. The hierarchical vocabulary structure acts as a multi-functional system where the same framework can be applied across different domains and tasks, reducing the need for separate domain-specific models while maintaining high accuracy.

The patent introduces domain-specific vocabularies as intermediary layers between the natural language input and the SQL generation process. These vocabularies act as mediators that translate domain concepts into structured SQL constructs. By inserting this intermediary vocabulary layer, the system manages complexity by breaking down the direct mapping challenge into manageable vocabulary-based transformations, improving accuracy without overwhelming system complexity.