EXPO-SQL improves Text-to-SQL by using clause-level rewards derived from execution error messages and incremental clause execution instead of uniform query-level rewards.
and Xie, Jinxia and Huang, Pengsheng
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
SPENCE shows older NL2SQL benchmarks like Spider have high performance sensitivity to syntactic changes, indicating likely training contamination, while newer ones like BIRD show little sensitivity and appear largely clean.
Progress-SQL introduces a multi-turn RL framework with ODT-based structural alignment and progressive rewards that measure improvement across refinement turns, yielding gains on BIRD, Spider, and robustness benchmarks.
citing papers explorer
-
EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
EXPO-SQL improves Text-to-SQL by using clause-level rewards derived from execution error messages and incremental clause execution instead of uniform query-level rewards.
-
SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks
SPENCE shows older NL2SQL benchmarks like Spider have high performance sensitivity to syntactic changes, indicating likely training contamination, while newer ones like BIRD show little sensitivity and appear largely clean.
-
Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards
Progress-SQL introduces a multi-turn RL framework with ODT-based structural alignment and progressive rewards that measure improvement across refinement turns, yielding gains on BIRD, Spider, and robustness benchmarks.