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In real enterprise analytics, a syntactically correct SQL query can return a value that is based on too small a sample, has an excessively wide confidence interval, or is too confounded to support action. Existing systems answer confidently in all such cases, a failure we quantify as the Unreliable Confident Answer Rate (UCAR). We contribute (1) a ten-category reliability taxonomy (R1-R10) covering hazards such as small-sample aggregates, multiple-comparison inflation, and distribution-tail mismatch; (2) a program-first data pipeline that generates 50,000 reliability-labeled training examples from a context-free grammar over public retail schemas, with schema-stratified SFT/GRPO splits; and (3) a controlled study of ","title":"ReliableTableQA:How Much Supervision Does Reliability Annotation Need?","url":"https://arxiv.org/abs/2607.20537","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.20537v1 Announce Type: cross \nAbstract: We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is answerable, but whether the computed answer is statistically meaningful. 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