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New research boosts Text-to-SQL accuracy with enhanced LLM reasoning

By PulseAugur Editorial · [4 sources] ·

Three new research papers published on arXiv explore advancements in Text-to-SQL technology, focusing on improving the accuracy and generalization of large language models (LLMs) in translating natural language questions into SQL queries. The papers introduce novel frameworks like CoTE-SQL, MapleDoctor, and Reward-SQL, which incorporate techniques such as self-enhanced reasoning, error detection and repair, and execution-aware rewards to tackle complex queries and enhance performance on benchmarks like Spider and Bird. These methods aim to make structured databases more accessible to non-expert users by improving the reliability and efficiency of LLM-driven SQL generation. AI

IMPACT These advancements in Text-to-SQL aim to improve data accessibility for non-experts by enhancing LLM accuracy and generalization in generating SQL queries.

RANK_REASON Three research papers published on arXiv detail new methods for Text-to-SQL generation.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New research boosts Text-to-SQL accuracy with enhanced LLM reasoning

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Three research papers published on arXiv detail new methods for Text-to-SQL generation.
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COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Esteban Schafir, Xu Zheng, Hojat Allah Salehi, Zhuomin Chen, Mo Sha, Wei Cheng, Dongsheng Luo ·

    DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL

    arXiv:2606.17821v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in translating natural language to SQL, yet existing methods still falter on complex queries requiring multi-step, data-aware reasoning. We introduce DecoSearch,…

  2. arXiv cs.AI TIER_1 English(EN) · Feng Lyu, Jinfeng Cen, Sijing Duan, Hao Wu, Shucheng Li, Weixu Zhang, Haolun Wu ·

    Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuning

    arXiv:2606.15598v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively. While recent advances in large language models (LLMs) have shown p…

  3. arXiv cs.AI TIER_1 English(EN) · Jiawei Shen, Chengcheng Wan, Ruoyi Qiao, Jiazhen Zou, Hang Xu, Yuchen Shao, Yueling Zhang, Weikai Miao, Geguang Pu ·

    Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors

    arXiv:2501.09310v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have been adopted for text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into SQL queries. However, such a technique faces correctne…

  4. arXiv cs.CL TIER_1 English(EN) · Yuxin Zhang, Meihao Fan, Ju Fan, Mingyang Yi, Yuyu Luo, Guoliang Li, Bin Wu, Wenchao Zhou ·

    Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards

    arXiv:2505.04671v3 Announce Type: replace Abstract: Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance. However, RL-based approaches still struggle with complex queries due to two key limitations: insuffi…

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