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<p>Current NL2SQL systems degrade sharply when confronted with practical constraints such as limited prompt length and the inability to fine-tune large language models (LLMs). Performance drop is especially pronounced in complex databases, where inaccurate schema linking, vague value conditions, and weak self-correction dominate the error surface. We propose Dynamic-SQL, an adaptive framework that couples multi-path chain-of-thought fusion with execution-based feedback correction. A dense–sparse hybrid vector space is first constructed to dynamically retrieve relevant schema elements, and an LLM is leveraged to generate an explicit schema subgraph. Real-value and few-shot exemplars are then injected to enrich the prompt and sharpen value conditioning. Multiple candidate SQL statements are produced via diverse reasoning paths; their chains of thought are fused to cover latent semantic interpretations, and execution feedback is exploited for iterative self-correction until convergence. On the BIRD benchmark, Dynamic-SQL, powered by the open source qwen2.5-coder-32b-instruct, reduces the average prompt length by <b>50.83%</b>, raises strict schema-linking recall from <b>72.63%</b> to <b>90.66%</b>, and achieves <b>63.23%</b> execution accuracy. By systematically addressing schema linking, exemplar augmentation, multi-path fusion reasoning, and self-correction, the framework offers a transferable paradigm for deploying LLMs in complex database querying scenarios.</p>

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Dynamic-SQL: an adaptive NL2SQL framework with multi-path fusion reasoning and execution feedback correction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Hongbin Hao,
  • Xin Zheng,
  • Xuhong Yu

摘要

Current NL2SQL systems degrade sharply when confronted with practical constraints such as limited prompt length and the inability to fine-tune large language models (LLMs). Performance drop is especially pronounced in complex databases, where inaccurate schema linking, vague value conditions, and weak self-correction dominate the error surface. We propose Dynamic-SQL, an adaptive framework that couples multi-path chain-of-thought fusion with execution-based feedback correction. A dense–sparse hybrid vector space is first constructed to dynamically retrieve relevant schema elements, and an LLM is leveraged to generate an explicit schema subgraph. Real-value and few-shot exemplars are then injected to enrich the prompt and sharpen value conditioning. Multiple candidate SQL statements are produced via diverse reasoning paths; their chains of thought are fused to cover latent semantic interpretations, and execution feedback is exploited for iterative self-correction until convergence. On the BIRD benchmark, Dynamic-SQL, powered by the open source qwen2.5-coder-32b-instruct, reduces the average prompt length by 50.83%, raises strict schema-linking recall from 72.63% to 90.66%, and achieves 63.23% execution accuracy. By systematically addressing schema linking, exemplar augmentation, multi-path fusion reasoning, and self-correction, the framework offers a transferable paradigm for deploying LLMs in complex database querying scenarios.