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Divide and Prompt: Chain of Thought Prompting for Text-to-SQL

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arxiv 2304.11556 v1 pith:VLGEVPRS submitted 2023-04-23 cs.CL cs.AI

Xiping Liu , Zhao Tan This is my paper
classification cs.CLcs.AI
keywords text-to-sqlpromptingllmsreasoningtaskscomplexlanguagetask
verification ladder T0 review T1 audit T2 compute T3 formal
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Chain-of-thought (CoT) prompting combined with large language models (LLMs) have achieved encouraging results on complex reasoning tasks. Text-to-SQL is a critical semantic parsing task that converts natural language questions into SQL statements, involving a complex reasoning process. However, there is little work about using CoT prompting to activate LLM's reasoning capabilities on Text-to-SQL tasks. In this work, we propose a new paradigm for prompting Text-to-SQL tasks, called Divide-and-Prompt, which first divides the task into subtasks, and then approach each subtask through CoT. We present 3 prompting-based methods to enhance the Text-to-SQL ability of LLMs. Experiments show that these prompts guide LLMs to generate Text-to-SQL with higher execution accuracy.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes

    cs.CL 2025-06 conditional novelty 6.0 of 10

    SDE-SQL improves text-to-SQL accuracy by having the model generate and execute exploratory SQL probes to learn database contents before and while writing the final query.

  2. Knowledge Base Construction for Knowledge-Augmented Text-to-SQL

    cs.CL 2025-05 conditional novelty 6.0 of 10

    KAT-SQL constructs a reusable knowledge base for text-to-SQL by expanding training data with LLM-generated knowledge and retrieving/refining the best entries for each query.

  3. Agentic LLMs for Question Answering over Tabular Data

    cs.CL 2025-09 conditional novelty 4.0 of 10

    A five-stage NL-to-SQL pipeline with GPT-4o achieves 70.5% on DataBench QA and 71.6% on DataBench Lite QA, beating baselines of 26% and 27%.

  4. Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A systematic review organizing LLM-based text-to-SQL methods into pre-processing, in-context learning, fine-tuning, and post-processing paradigms, with a catalog of datasets, metrics, challenges, and future directions.

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