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arXiv · 2503.19988

ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback

Bohan Zhai·Canwen Xu·Yuxiong He·Zhewei Yao
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Abstract

Text-to-SQL demands precise reasoning to convert natural language questions into structured queries. While large language models (LLMs) excel in many reasoning tasks, their ability to leverage Chain-of-Thought (CoT) reasoning for text-to-SQL remains underexplored. We identify critical limitations: zero-shot CoT offers minimal gains, and Direct Preference Optimization (DPO) applied without CoT yields marginal improvements. We propose ExCoT, a novel framework that iteratively optimizes open-source LLMs by combining CoT reasoning with off-policy and on-policy DPO, relying solely on execution accuracy as feedback. This approach eliminates the need for reward models or human-annotated preferences. Our experimental results demonstrate significant performance gains: ExCoT improves execution accuracy on BIRD dev set from 57.37% to 68.51% and on Spider test set from 78.81% to 86.59% for LLaMA-3 70B, with Qwen-2.5-Coder demonstrating similar improvements. Our best model achieves state-of-the-art performance in the single-model setting on both BIRD and Spider datasets, notably achieving 68.53% on the BIRD test set.

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Bohan Zhai, Canwen Xu, Yuxiong He, Zhewei Yao. 2025-03-25. ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback. https://arxiv.org/abs/2503.19988

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