ARCS: Towards Precise Text-to-SQL via Structured Disambiguation

2026-10-06

AI Summary

This study addresses the problem of Text-to-SQL execution errors caused by ambiguous user queries in real-world scenarios, where traditional conversational clarification proves inefficient. To this end, this work proposes a novel structured disambiguation paradigm that efficiently resolves semantic ambiguity through explicitly constrained interactions. The primary contributions include the construction of ARCS, the first real-world benchmark dataset featuring natural ambiguity annotations with comprehensive coverage of ambiguity points, clarifications, and SQL labels, alongside an end-to-end framework integrating large-scale semantic annotation with automated evaluation. Experiments reveal significant limitations of existing models in handling ambiguity: the best-performing closed-source model achieves only 51% accuracy, while open-source counterparts fall below 27%.

Abstract

As text-to-SQL systems move beyond demonstrations toward real-world deployment, ambiguity in user questions becomes a primary source of errors. Such ambiguities are often subtle, domain- or data-specific, and can silently cause system outputs to deviate from the user's true intent. Ambiguity is traditionally addressed through conversational clarification, which is often inefficient, cognitively demanding, and poorly aligned with real-world user workflows. We propose structured disambiguation, a new paradigm in which ambiguity is resolved through explicit, constrained interactions rather than free-form dialogue. We construct ARCS (Ambiguity Resolution Corpus for SQL), the first text-to-SQL benchmark featuring naturally occurring, unconstrained ambiguities over real-world databases, with complete annotations of all valid ambiguity points, interpretations, and SQL queries. Experimental results show that text-to-SQL remains challenging in the presence of ambiguity: gpt-6-sol achieves only 51% end-to-end execution accuracy, and no open-source model exceeds 27%.

Problem

• Text-to-SQL
• Ambiguity Resolution
• Disambiguation
• Benchmark

Innovation

• Structured Disambiguation
• Text-to-SQL
• Ambiguity Resolution
• Benchmark
• ARCS