Synthetic NL2SQL Training Data Framework for Cross-Database Generalization
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Solution Overview
Problem
Existing natural language to SQL (NL2SQL) models require large amounts of manually curated training data and struggle to generalize across different databases, leading to inefficient and costly data collection processes.
Innovation Solution
A data manufacturing framework that synthesizes synthetic training data using techniques such as synchronous context-free grammar, probabilistic context-free grammar, and tree-to-string translation to generate large-scale training data, enabling effective training of NL2SQL models across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually curated training data is used for NL2SQL models, then model training accuracy is improved, but data collection cost and time increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating synthetic training data using grammar-based methods before model training begins. This pre-generation of diverse SQL query examples with corresponding natural language descriptions eliminates the need for time-consuming manual data curation while providing sufficient training material for accurate model learning.
Solution Approach 2:
The system creates copies of existing database schemas and query patterns to generate synthetic training data. By replicating and transforming real database structures into synthetic versions with varied query examples, the system produces large volumes of training data that mimic real-world scenarios without requiring actual manual collection, thus reducing time while maintaining training quality.
2Measurement precision
If manually curated training data is used for NL2SQL models, then model training accuracy is improved, but data collection cost increases
Solution Approach 1:
The system creates synthetic copies of training data by automatically generating SQL queries and their natural language descriptions from database schemas. This copying approach produces unlimited training examples at minimal computational cost, eliminating expensive manual annotation processes while maintaining the diversity and quality needed for accurate model training.
Solution Approach 2:
The system uses inexpensive automated grammar-based generation methods to create disposable synthetic training data. These generated examples can be produced in large quantities at very low cost and discarded or regenerated as needed, replacing the need for expensive, irreplaceable manually curated datasets.
3Reliability
If NL2SQL models are trained on domain-specific data, then performance on specific domains is improved, but generalization to new domains deteriorates
Solution Approach 1:
The system employs a universal grammar-based generation framework that can produce training data for multiple domains simultaneously. By using domain-agnostic grammatical rules to generate SQL queries and natural language descriptions, the system creates training data that teaches models both domain-specific patterns and general query formulation skills, enabling the model to adapt to new domains without retraining.
Solution Approach 2:
The system dynamically adapts the synthetic data generation process to different domains by adjusting grammar parameters and database schema configurations. This dynamic approach allows the same generation framework to produce domain-appropriate training data on-demand, teaching the model to handle various domain-specific characteristics while maintaining generalization capabilities through exposure to diverse query patterns.
4Adaptability or versatility
If deep learning models are used for NL2SQL translation, then generalization to new use cases is improved, but training data requirements increase
Solution Approach 1:
The system performs preliminary data generation using grammar-based methods to create large volumes of synthetic training data before deep learning model training. This pre-generation ensures that sufficient diverse training examples are available to train deep learning models effectively, enabling the models to achieve good generalization performance without requiring extensive manual data collection.
Data Source
AI summary
Techniques are disclosed herein for synthesizing synthetic training data to facilitate training a natural language to logical form model. In one aspect, training data can be synthesized from original under a framework based on templates and a synchronous context-free grammar. In one aspect, training data can be synthesized under a framework based on a probabilistic context-free grammar and a translator. In one aspect, training data can be synthesized under a framework based on tree-to-string translation. In one aspect, the synthetic training data can be combined with original training data in order to train a machine learning model to translate an utterance to a logical form.