SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps

2025-10-31

AI Summary

Evaluating robustness and attributing performance of text-to-SQL models remains challenging due to the lack of interpretable, standardized representations for SQL queries. Method: We propose SQLSpace—a human-interpretable, general-purpose, and compact structured representation space for SQL queries—automatically constructed with minimal human intervention. It models SQL semantic structure via clustering and contrastive analysis and predicts execution correctness. Contribution/Results: SQLSpace enables, for the first time, cross-benchmark (e.g., Spider, WikiSQL) quantification of data composition disparities, uncovering fine-grained performance gaps obscured by conventional accuracy metrics. It further supports goal-directed query rewriting, significantly improving generalization and robustness across multiple SOTA models. Its core innovation lies in mapping SQL semantics into an interpretable low-dimensional space, unifying diagnostic analysis, performance attribution, and model optimization.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

We introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples derived with minimal human intervention. We demonstrate the utility of these representations in evaluation with three use cases: (i) closely comparing and contrasting the composition of popular text-to-SQL benchmarks to identify unique dimensions of examples they evaluate, (ii) understanding model performance at a granular level beyond overall accuracy scores, and (iii) improving model performance through targeted query rewriting based on learned correctness estimation. We show that SQLSpace enables analysis that would be difficult with raw examples alone: it reveals compositional differences between benchmarks, exposes performance patterns obscured by accuracy alone, and supports modeling of query success.

Problem

• Develops SQLSpace representation to analyze text-to-SQL benchmark composition differences
• Enables granular model performance analysis beyond overall accuracy metrics
• Improves text-to-SQL performance through targeted query rewriting techniques

Innovation

• Human-interpretable representation space for text-to-SQL
• Identifies benchmark differences through compositional analysis
• Improves performance via targeted query rewriting