Which execution oracle labels a conformal abstention certificate's calibration set determines the risk the certificate reports, and neither published oracle matches what SQL experts judge.
Reliable text-to-sql with adaptive abstention.Proc
4 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
years
2026 4roles
background 1polarities
background 1representative citing papers
Residual skill optimization creates complementary Text-to-SQL agents by training each new skill on prior ensemble failures, yielding accuracy gains on Spider2-Lite and transfer to other dialects and tasks.
Reliability annotation for tabular QA reaches near-perfect accuracy with about 200 schema-stratified SFT examples, and GRPO adds measurable value only when SFT is undertrained.
SPA trains LLMs via plan-aware RL with adaptive reward shaping and self-improvement on slowdowns to produce faster query rewrites than rule-based or standard LLM methods on IID and OOD workloads.
citing papers explorer
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Certified Against Which Oracle? Execution Labels Set the Reported Risk of Conformal Abstention for Text-to-SQL
Which execution oracle labels a conformal abstention certificate's calibration set determines the risk the certificate reports, and neither published oracle matches what SQL experts judge.
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Residual Skill Optimization for Text-to-SQL Ensembles
Residual skill optimization creates complementary Text-to-SQL agents by training each new skill on prior ensemble failures, yielding accuracy gains on Spider2-Lite and transfer to other dialects and tasks.
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ReliableTableQA:How Much Supervision Does Reliability Annotation Need?
Reliability annotation for tabular QA reaches near-perfect accuracy with about 200 schema-stratified SFT examples, and GRPO adds measurable value only when SFT is undertrained.
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SPA: A SQL-Plan-Aware Reinforcement Learning Framework for Query Rewriting with LLMs
SPA trains LLMs via plan-aware RL with adaptive reward shaping and self-improvement on slowdowns to produce faster query rewrites than rule-based or standard LLM methods on IID and OOD workloads.