Query Carefully: Detecting the Unanswerables in Text-to-SQL Tasks

• URL: http://arxiv.org/abs/2512.21345v1
• Date: Fri, 19 Dec 2025 12:22:27 GMT
• Title: Query Carefully: Detecting the Unanswerables in Text-to-SQL Tasks
• Authors: Jasmin Saxer, Isabella Maria Aigner, Luise Linzmeier, Andreas Weiler, Kurt Stockinger,
• Abstract summary: Text-to- systems allow non- experts to interact with databases using natural language.<n>Their tendency to generate executablesql for ambiguous, out-of-scope, or unanswerable queries introduces a hidden risk, as outputs may be misinterpreted as correct.<n>We present Query, a pipeline that integratessql generation with explicit ambiguity and handling of unanswerable inputs.
• Score: 1.7781743265224403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-SQL systems allow non-SQL experts to interact with relational databases using natural language. However, their tendency to generate executable SQL for ambiguous, out-of-scope, or unanswerable queries introduces a hidden risk, as outputs may be misinterpreted as correct. This risk is especially serious in biomedical contexts, where precision is critical. We therefore present Query Carefully, a pipeline that integrates LLM-based SQL generation with explicit detection and handling of unanswerable inputs. Building on the OncoMX component of ScienceBenchmark, we construct OncoMX-NAQ (No-Answer Questions), a set of 80 no-answer questions spanning 8 categories (non-SQL, out-of-schema/domain, and multiple ambiguity types). Our approach employs llama3.3:70b with schema-aware prompts, explicit No-Answer Rules (NAR), and few-shot examples drawn from both answerable and unanswerable questions. We evaluate SQL exact match, result accuracy, and unanswerable-detection accuracy. On the OncoMX dev split, few-shot prompting with answerable examples increases result accuracy, and adding unanswerable examples does not degrade performance. On OncoMX-NAQ, balanced prompting achieves the highest unanswerable-detection accuracy (0.8), with near-perfect results for structurally defined categories (non-SQL, missing columns, out-of-domain) but persistent challenges for missing-value queries (0.5) and column ambiguity (0.3). A lightweight user interface surfaces interim SQL, execution results, and abstentions, supporting transparent and reliable text-to-SQL in biomedical applications.

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