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New methods boost Text-to-SQL accuracy with execution feedback

By PulseAugur Editorial · [8 sources] ·

Researchers have developed several new methods to improve Text-to-SQL systems, which translate natural language questions into SQL queries. These approaches focus on enhancing schema linking and leveraging execution feedback to refine SQL generation. Techniques like GATE, ACE-SQL, CAPER, and SIRIUS-SQL aim to address challenges posed by complex database schemas and underspecified queries, leading to more accurate and robust SQL outputs. AI

IMPACT These advancements in Text-to-SQL systems could significantly improve data accessibility and analysis for non-technical users by enabling more accurate and reliable natural language querying of databases.

RANK_REASON Multiple academic papers introducing new methods for Text-to-SQL systems.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 8 sources. How we write summaries →

New methods boost Text-to-SQL accuracy with execution feedback

COVERAGE [8]

  1. arXiv cs.AI TIER_1 English(EN) · Shihao Zhang, Xiaoman Wang, Yuan Liu, Yunshi Lan, Weining Qian ·

    Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards

    arXiv:2606.06825v1 Announce Type: cross Abstract: Reinforcement learning has recently shown promise in improving large language models for Text-to-SQL generation, yet existing methods typically optimize one-shot rewards defined over a single SQL state. Such rewards provide limite…

  2. arXiv cs.CL TIER_1 English(EN) · Youngwon Lee, Jaejin Kim, Seung-won Hwang ·

    Bootstrapping Semantic Layer from Execution for Text-to-SQL

    arXiv:2606.05634v1 Announce Type: new Abstract: Real-world text-to-SQL is often under-specified until user phrases are grounded in how the database stores values. Prior work attempts to address this by requiring a semantic layer to specify groundings in advance, but such specific…

  3. arXiv cs.CL TIER_1 English(EN) · Xiaobing Chen, Ai Jian, Eryu Guo, Zhiqi Pang ·

    ACE-SQL: Adaptive Co-Optimization via Empirical Credit Assignment for Text-to-SQL

    arXiv:2606.05906v1 Announce Type: new Abstract: Text-to-SQL maps natural language questions to executable SQL queries. Modern databases often contain large and complex schemas, making schema linking a critical step for accurate SQL generation. Existing methods either rely on full…

  4. arXiv cs.CL TIER_1 English(EN) · Weining Qian ·

    Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards

    Reinforcement learning has recently shown promise in improving large language models for Text-to-SQL generation, yet existing methods typically optimize one-shot rewards defined over a single SQL state. Such rewards provide limited guidance for iterative SQL correction and are in…

  5. arXiv cs.CL TIER_1 English(EN) · Zhiqi Pang ·

    ACE-SQL: Adaptive Co-Optimization via Empirical Credit Assignment for Text-to-SQL

    Text-to-SQL maps natural language questions to executable SQL queries. Modern databases often contain large and complex schemas, making schema linking a critical step for accurate SQL generation. Existing methods either rely on full-schema generation, which leaves schema linking …

  6. arXiv cs.CL TIER_1 English(EN) · Lujie Ban, Jiasheng Shi, Jinyang Li, Xiaolin Han, Tsz Nam Chan, Chenhao Ma ·

    CAPER: Clause-Aligned Process Supervision for Text-to-SQL

    arXiv:2606.03327v1 Announce Type: cross Abstract: Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which intermediate SQL decision caused success or failure. Token-level dense supervision is …

  7. arXiv cs.CL TIER_1 English(EN) · Chenhao Ma ·

    CAPER: Clause-Aligned Process Supervision for Text-to-SQL

    Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which intermediate SQL decision caused success or failure. Token-level dense supervision is also ill-suited: SQL tokens do not align with comp…

  8. arXiv cs.AI TIER_1 English(EN) · Leo Luo, Haining Xie, Siqi Shen, Zhipeng Ma, Rui Ling, Hang Xu, Hefeng Jiang, Dingwei Chen, Yang Li, Peng Chen, Jie Jiang ·

    SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback

    arXiv:2606.01246v1 Announce Type: new Abstract: Text-to-SQL on complex schemas is unreliable on a single pass, so recent systems generate multiple SQL candidates and let voting filter out errors. Yet voting alone is not enough, because the multi-candidate recipe has three coupled…

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