ARCS: Towards Precise Text-to-SQL via Structured Disambiguation
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Computer Science > Computation and Language
arXiv:2610.09396 (cs)
[Submitted on 7 Oct 2026]
Title:ARCS: Towards Precise Text-to-SQL via Structured Disambiguation
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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%.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.09396 [cs.CL] |
| (or arXiv:2610.09396v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09396
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ARCS: Towards Precise Text-to-SQL via Structured Disambiguation, by Yihao Hu and 3 other authors
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