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DivSkill-SQL boosts Text-to-SQL ensembles with complementary agent training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DivSkill-SQL, a novel framework for enhancing Text-to-SQL ensembles. This method optimizes complementary skills by training new agents on examples that the existing ensemble fails on, thereby increasing the probability of generating at least one correct SQL candidate. The framework demonstrated significant improvements, boosting accuracy by up to 11.1 points on Snowflake and 8.3 points on BigQuery when tested with Opus-4.6 and GPT-5.4 base models on the Spider2-Lite dataset. Notably, these optimized skills showed transferability across different SQL dialects and task formulations, with error analysis indicating a reduction in hallucinations and more reliable complementary skills. AI

IMPACT Enhances accuracy and reliability of Text-to-SQL systems, potentially improving data access and analysis for AI applications.

RANK_REASON The cluster contains an academic paper detailing a new method for improving Text-to-SQL models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DivSkill-SQL boosts Text-to-SQL ensembles with complementary agent training

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The cluster contains an academic paper detailing a new method for improving Text-to-SQL models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jiongli Zhu, Haoquan Guan, Parjanya Prajakta Prashant, Nikki Lijing Kuang, Seyedeh Baharan Khatami, Canwen Xu, Xiaodong Yu, Yingyu Lin, Zhewei Yao, Yuxiong He, Babak Salimi ·

    Residual Skill Optimization for Text-to-SQL Ensembles

    arXiv:2605.21792v1 Announce Type: new Abstract: Text-to-SQL ensembles improve over single-candidate generation by drawing multiple SQL candidates and selecting one, but their effectiveness is bounded by Pass@K, the probability that at least one of K candidates is correct. Existin…

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