EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement
Schema ambiguity remains a critical bottleneck for natural language database querying at scale. This work reframes schema refinement as an optimization problem solvable through execution-grounded feedback, using database views to preserve query semantics while improving naming clarity. The greedy decomposition approach addresses computational hardness and offers a practical pipeline for enterprises deploying text-to-SQL systems on legacy or poorly-documented databases. The strategic value lies in bridging the gap between LLM capabilities and real-world schema chaos, a friction point that has limited adoption of conversational database interfaces in production environments.
Modelwire context
ExplainerThe key innovation is reframing schema refinement as an optimization problem solved via database views rather than direct schema modification. This preserves query semantics while improving naming clarity, which sidesteps the risk of breaking existing applications during schema cleanup.
This work sits within a broader pattern visible across recent research: decomposing LLM workflows into stages with explicit validation gates. The chart generation paper from May treated visualization as a multi-stage pipeline with intermediate inspection points; EGREFINE does the same for database querying, using execution feedback as the validation signal. Both recognize that single-pass LLM inference fails on structured tasks requiring correctness guarantees. The constraint-guided execution in RunAgent (also May) shares the same philosophy: trading some flexibility for determinism in domains where failure tolerance is low. For text-to-SQL specifically, schema ambiguity has been the adoption blocker in production, and this addresses it head-on by making the problem tractable without requiring perfect upstream documentation.
If enterprises deploying EGREFINE report that execution-grounded refinement reduces query failure rates by more than 30% on legacy schemas without manual annotation, that confirms the approach generalizes beyond the benchmark. Otherwise, watch whether the greedy decomposition strategy proves too conservative on real-world schemas with deep interdependencies.
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MentionsEGRefine · Text-to-SQL
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