mlsanityData to 14 Oct 2026

R3: "This is My SQL, Are You With Me?" A Consensus-Based Multi-Agent System for Text-to-SQL Tasks

arXiv 2402.14851 ↗First posted 20 Feb 2024cs.CL cs.AI cs.DBcited by 7

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Large Language Models (LLMs) have demonstrated strong performance on various tasks. To unleash their power on the Text-to-SQL task, we propose R3 (Review-Rebuttal-Revision), a consensus-based multi-agent system for Text-to-SQL tasks. R3 outperforms the existing single LLM Text-to-SQL systems as well as the multi-agent Text-to-SQL systems by 1.3% to 8.1% on Spider and Bird. Surprisingly, we find that for Llama-3-8B, R3 outperforms chain-of-thought prompting by over 20%, even outperforming GPT-3.5 on the development set of Spider.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 24 references that are in the map. First posted on arXiv 20 Feb 2024.

Builds on

14 of 24 references.

References · 14

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  4. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  6. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  7. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
  8. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  9. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  10. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  11. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 164
  12. 2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 152
2 more
  1. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  2. 2023You Are What You Annotate: Towards Better Models through Annotator Representationscited by 40

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
  2. 2024A Survey on Employing Large Language Models for Text-to-SQL Taskscited by 67
  3. 2025SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQLcited by 16
  4. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  5. 2025Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languagescited by 3
  6. 2025Distill-C: Enhanced NL2SQL via Distilled Customization with LLMscited by 1
  7. 2026Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQLcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025MTSQL-R1: Towards Long-Horizon Multi-Turn Text-to-SQL via Agentic Training
  2. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
  3. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  4. 2025MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL
  5. 2025Multilingual Text-to-SQL: Benchmarking the Limits of Language Models with Collaborative Language Agents
  6. 2026Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL
  7. 2025A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback
  8. 2026AgentSM: Semantic Memory for Agentic Text-to-SQL