mlsanityData to 7 Oct 2026

SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL

arXiv 2409.10007 ↗First posted 16 Sep 2024cs.CL cs.AIcited by 4

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

Abstract (arXiv)

In recent years,Text-to-SQL, the problem of automatically converting questions posed in natural language to formal SQL queries, has emerged as an important problem at the intersection of natural language processing and data management research. Large language models (LLMs) have delivered impressive performance when used in an off-the-shelf performance, but still fall significantly short of expected expert-level performance. Errors are especially probable when a nuanced understanding is needed of database schemas, questions, and SQL clauses to do proper Text-to-SQL conversion. We introduce SelECT-SQL, a novel in-context learning solution that uses an algorithmic combination of chain-of-thought (CoT) prompting, self-correction, and ensemble methods to yield a new state-of-the-art result on challenging Text-to-SQL benchmarks. Specifically, when configured using GPT-3.5-Turbo as the base LLM, SelECT-SQL achieves 84.2% execution accuracy on the Spider leaderboard's development set, exceeding both the best results of other baseline GPT-3.5-Turbo-based solutions (81.1%), and the peak performance (83.5%) of the GPT-4 result reported on the leaderboard.

Key facts. Cited by 4 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 22 of its 29 references that are in the map. First posted on arXiv 16 Sep 2024.

Builds on

22 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 21

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  5. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  6. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  7. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  8. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  9. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  10. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  11. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 378
  12. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 159
9 more
  1. 2023Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Stepcited by 144
  2. 2023Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Frameworkcited by 139
  3. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  4. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  5. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  6. 2023How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settingscited by 57
  7. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  8. 2023Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategiescited by 44
  9. 2021SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoisingcited by 27

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Enhancing Text2Cypher with Schema Filteringcited by 6
  2. 2025SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQLcited by 4
  3. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  4. 2026Integrating Reasoning and Generalization in Text-to-SQL via Self-Enhanced Fine-Tuningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
  2. 2023ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
  3. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL
  4. 2024Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models
  5. 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL
  6. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
  7. 2025GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models
  8. 2025Confidence Estimation for Error Detection in Text-to-SQL Systems