SemanticAgent: New Framework Improves Text-to-SQL Data Synthesis
A new research paper presents SemanticAgent, a framework designed for text-to-SQL data synthesis with a focus on semantics. Traditional pipelines often mix up executability with semantic correctness, as they may pass queries that execute successfully but fail to adhere to database semantics. To tackle this issue, SemanticAgent is structured around three distinct modules: an analyzer, a synthesizer, and a verifier. It follows a three-phase protocol involving semantic analysis, progressive synthesis, and diagnostic refinement, turning execution-based validation into a traceable reasoning approach. The framework produces synthetic data that consistently surpasses previous techniques in semantic-quality assessments, enhancing downstream fine-tuning performance, particularly on challenging semantic benchmarks. The paper can be found on arXiv in the Computer Science > Artificial Intelligence section.
Key facts
- SemanticAgent is a semantics-aware framework for text-to-SQL data synthesis.
- Existing pipelines conflate executability with semantic validity.
- Syntactic checks and execution-based validation can retain semantically invalid queries.
- SemanticAgent uses three modules: analyzer, synthesizer, and verifier.
- It employs a three-stage protocol: semantic analysis, stepwise synthesis, and diagnostic refinement.
- The framework transforms execution-based validation into a traceable reasoning process.
- SemanticAgent outperforms prior methods under semantic-quality evaluation.
- It leads to stronger downstream fine-tuning performance on semantically demanding benchmarks.
Entities
Institutions
- arXiv