DPC: Training-Free Text-to-SQL Candidate Selection via Dual-Paradigm Consistency
Nov 2025 – Jan 2026 · ACL 2026 MainDeveloped DPC (Dual-Paradigm Consistency) to address the gap between LLMs' strong SQL generation and weak candidate-selection capabilities, reframing selection from probabilistic judgment as deterministic verification. The framework uses multi-agent collaboration and an adversarial feedback loop to synthesize Minimal Distinguishing Databases (MDDs), while exploiting the complementary execution logic of SQL and Python to construct model-independent reference anchors. Integrated as a plug-and-play module into state-of-the-art systems including DAIL-SQL and CHESS, DPC improves execution accuracy by an average of 1.8%. As second author, I conducted experiments across multiple models and systems, performed in-depth case analyses, and contributed to manuscript revision.