SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis

2026-04-23

AI Summary

Existing text-to-SQL synthesis methods often conflate executability with semantic correctness, yielding queries that execute successfully yet violate the underlying database semantics. This work proposes the first framework to explicitly model semantic validity within the synthesis pipeline, introducing a modular architecture comprising an analyzer, synthesizer, and validator. These components jointly enable a three-stage reasoning process—semantic parsing, stepwise query synthesis, and diagnostic refinement—transforming execution-based validation into traceable semantic inference. By enforcing semantic consistency throughout generation, the approach substantially outperforms state-of-the-art methods on multiple high-complexity benchmarks and significantly enhances downstream fine-tuning performance.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Existing text-to-SQL synthesis pipelines still conflate executability with semantic validity: syntactic checks and execution-based validation can retain queries that execute successfully while violating database semantics. To address these limitations, we propose SemanticAgent, a semantic-aware synthesis framework. SemanticAgent organizes synthesis around three specialized modules: an analyzer, a synthesizer, and a verifier. Through a three-stage protocol of semantic analysis, stepwise synthesis, and diagnostic refinement, SemanticAgent transforms execution-based validation alone into a traceable reasoning process. Our framework generates synthetic data that consistently outperforms prior synthesis methods under semantic-quality evaluation, leading to stronger downstream fine-tuning performance, especially on semantically demanding benchmarks.

Problem

• text-to-SQL
• semantic validity
• data synthesis
• database semantics
• executability

Innovation

• SemanticAgent
• text-to-SQL
• semantic validity
• data synthesis
• traceable reasoning