TAHOE: New System Improves Text-to-SQL Performance Through Automated Hint Optimization
Researchers introduced TAHOE, a system that optimizes how large language models convert natural language queries into SQL code by learning from errors and building reusable hints. The system separates syntax hints (for database dialect rules) from semantic hints (for schema and user preferences), improving accuracy without retraining model parameters. This addresses a key challenge in deploying Text-to-SQL systems to production environments where strict requirements and large databases make current approaches impractical.
TAHOE treats prompt optimization as a dynamic data management problem, using an error-driven pipeline to consolidate debugging information into a structured Hint Bank during development. The system distills compiler feedback into reusable Syntax Hints for dialect-specific SQL rules and converts execution and user feedback into Semantic Hints for schema- and user-specific logic. A Strategy Layer models conflicting user intents as competing strategies with recency signals and attribution statistics. Testing on Spider 2.0-Snow benchmarks with GPT-4.5 showed substantial improvements: pass rate increased from 61.95% to 79.42%, pass-at-4 improved from 72.57% to 87.61%, and compiler-feedback rounds dropped from 2.79 to 0.12 per candidate. The hint bank also transferred to weaker models, demonstrating a 19.7 percentage-point gain on Doubao-2.0-lite.
What's missing
The paper notes that deployment-time human-feedback updates are left for future work, indicating the current evaluation is limited to development-phase workflows. The study does not discuss computational costs of the hint retrieval and planning process at inference time, or how performance scales with very large hint banks.
What different sources said
- arXiv cs.AICenter
TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
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