mlsanityData to 7 Oct 2026

PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL

arXiv 2409.14082 ↗First posted 21 Sep 2024cs.CL cs.AIcited by 7

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Large Language Models (LLMs) have emerged as powerful tools for Text-to-SQL tasks, exhibiting remarkable reasoning capabilities. Different from tasks such as math word problems and commonsense reasoning, SQL solutions have a relatively fixed pattern. This facilitates the investigation of whether LLMs can benefit from categorical thinking, mirroring how humans acquire knowledge through inductive reasoning based on comparable examples. In this study, we propose that employing query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type, consequently enhancing their reasoning abilities across diverse difficulty levels and problem categories. Our experiments reveal that multiple advanced LLMs, when equipped with PTD-SQL, can either surpass or match previous state-of-the-art (SOTA) methods on the Spider and BIRD datasets. Intriguingly, models with varying initial performances have exhibited significant improvements, mainly at the boundary of their capabilities after targeted drilling, suggesting a parallel with human progress. Code is available at https://github.com/lrlbbzl/PTD-SQL.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 36 of its 43 references that are in the map. First posted on arXiv 21 Sep 2024.

Builds on

36 of 43 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm16 Feb
  2. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning31 Jan

Other references · 34

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  5. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  6. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,154
  7. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  8. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  9. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
  10. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  11. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  12. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
22 more
  1. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  2. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  3. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  4. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
  5. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  6. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 159
  7. 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
  8. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 123
  9. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  10. 2024Self-Discover: Large Language Models Self-Compose Reasoning Structurescited by 108
  11. 2024MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generationcited by 103
  12. 2024CriticBench: Benchmarking LLMs for Critique-Correct Reasoningcited by 88
  13. 2024Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generationcited by 49
  14. 2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
  15. 2024MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQLcited by 34
  16. 2024SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Modelscited by 32
  17. 2024AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queriescited by 32
  18. 2021SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoisingcited by 27
  19. 2024HyperMoE: Towards Better Mixture of Experts via Transferring Among Expertscited by 25
  20. 2024Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrastcited by 19
  21. 2023Small Language Models Fine-tuned to Coordinate Larger Language Models improve Complex Reasoningcited by 16
  22. 2024LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
  2. 2025BASE-SQL: A powerful open source Text-To-SQL baseline approachcited by 9
  3. 2025DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQLcited by 5
  4. 2025SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQLcited by 4
  5. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  6. 2026SQLStructEval: Structural Evaluation of LLM Text-to-SQL Generationcited by 1
  7. 2026Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuningcited by 0

Similar, not cited

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  1. 2024Lucy: Think and Reason to Solve Text-to-SQL
  2. 2025Weaver: Interweaving SQL and LLM for Table Reasoning
  3. 2025SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs
  4. 2025RAISE: Reasoning Agent for Interactive SQL Exploration
  5. 2025SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
  6. 2026ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
  7. 2025When Reasoning Beats Scale: A 1.5B Reasoning Model Outranks 13B LLMs as Discriminator
  8. 2024Multi-Turn Interactions for Text-to-SQL with Large Language Models