EXPO-SQL introduces clause-level execution feedback to improve Text-to-SQL models
New reinforcement learning method assigns fine-grained rewards at the clause level to address coarse-grained reward limitations in Text-to-SQL generation.
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- EXPO-SQL proposes clause-level execution feedback to improve Text-to-SQL generation using large language models.
- The method outperforms supervised fine-tuning, prompting, and RL baselines on widely-used Text-to-SQL benchmarks.
- Code and implementation are available on GitHub.
Researchers propose EXPO-SQL, a reinforcement learning method that assigns clause-level rewards for Text-to-SQL generation, addressing the limitation of coarse-grained query-level rewards in existing approaches.
The method identifies erroneous clauses by analyzing execution results, including error messages and clause-wise incremental execution, to provide fine-grained supervision during training.
Experiments on widely-used Text-to-SQL benchmarks show that EXPO-SQL significantly outperforms existing supervised fine-tuning, prompting, and reinforcement learning baselines through clause-level learning.
The authors release code and implementation on GitHub under the identifier EXPO-SQL.
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