Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model

Background

Text-to-SQL converts natural language questions into executable SQL queries, enabling non-technical users to access relational databases for analytics. However, this process faces significant constraints in low-resource settings where high-quality annotated data are scarce. The challenges include opaque schema definitions, abbreviations, and implicit business logic that aren't explicitly encoded in the schema.

Key Points

The article introduces a knowledge-aware Text-to-SQL framework designed to tackle these issues. This framework:

• Constructs a task-specific knowledge base that includes schema semantics, abbreviations, business logic, and query patterns.
• Injects this knowledge into both training and inference processes to generate diverse, contextually grounded synthetic training data.
• Enhances inference through targeted knowledge retrieval.

The key insights include the importance of incorporating domain-specific knowledge into model training and enhancing generalization and robustness in low-resource settings. The framework addresses the limitations of existing data synthesis and prompting techniques by aligning generated examples more closely with database constraints.

Significance

Experiments on seven benchmarks, covering both general and domain-specific datasets, demonstrate that this approach significantly improves performance, especially in low-resource domain-specific settings. Key outcomes include:

• Enhanced Generalization: The framework helps models generalize better from synthetic data to real-world queries.
• Improved Robustness: By considering schema semantics and business logic, the model's responses are more robust and contextually appropriate.
• Increased Adaptability: The targeted knowledge retrieval process enables the model to adapt to specific database contexts.

These improvements make Text-to-SQL more viable for non-technical users in a wide range of domains where high-quality annotated data are limited.