# A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL

arXiv: https://arxiv.org/abs/2411.08599 · HTML: https://mlsanity.com/paper/2411.08599/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2024.

## Built on by (most cited first, 30 of 46)

- [Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL](https://mlsanity.com/paper/2503.23157/) (2025)
- [XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL](https://mlsanity.com/paper/2507.04701/) (2025)
- [Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL](https://mlsanity.com/paper/2505.20315/) (2025)
- [ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback](https://mlsanity.com/paper/2503.19988/) (2025)
- [SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL](https://mlsanity.com/paper/2502.11741/) (2025)
- [AutoLink: Autonomous Schema Exploration and Expansion for Scalable Schema Linking in Text-to-SQL at Scale](https://mlsanity.com/paper/2511.17190/) (2025)
- [SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications](https://mlsanity.com/paper/2506.18951/) (2025)
- [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://mlsanity.com/paper/2505.13271/) (2025)
- [MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL](https://mlsanity.com/paper/2511.01008/) (2025)
- [ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL](https://mlsanity.com/paper/2412.10138/) (2024)
- [Cheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning](https://mlsanity.com/paper/2505.14174/) (2025)
- [ST-Raptor: LLM-Powered Semi-Structured Table Question Answering](https://mlsanity.com/paper/2508.18190/) (2025)
- [Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL](https://mlsanity.com/paper/2511.10192/) (2025)
- [Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation](https://mlsanity.com/paper/2502.12911/) (2025)
- [ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL](https://mlsanity.com/paper/2505.12768/) (2025)
- [LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning](https://mlsanity.com/paper/2505.18744/) (2025)
- [CoddLLM: Empowering Large Language Models for Data Analytics](https://mlsanity.com/paper/2502.00329/) (2025)
- [Automatic database description generation for Text-to-SQL](https://mlsanity.com/paper/2502.20657/) (2025)
- [Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation](https://mlsanity.com/paper/2502.21107/) (2025)
- [On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows](https://mlsanity.com/paper/2504.01931/) (2025)
- [SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation](https://mlsanity.com/paper/2506.07423/) (2025)
- [RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System](https://mlsanity.com/paper/2508.17590/) (2025)
- [SpotIt: Evaluating Text-to-SQL Evaluation with Formal Verification](https://mlsanity.com/paper/2510.26840/) (2025)
- [SAFENLIDB: A Privacy-Preserving Safety Alignment Framework for LLM-based Natural Language Database Interfaces](https://mlsanity.com/paper/2511.06778/) (2025)
- [Same Content, Different Representations: A Controlled Study for Table QA](https://mlsanity.com/paper/2509.22983/) (2025)
- [Closing the Reflection Gap: A Free Calibration Bonus for Agentic RL](https://mlsanity.com/paper/2606.14211/) (2026)
- [Rationalization Models for Text-to-SQL](https://mlsanity.com/paper/2502.06759/) (2025)
- [Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models](https://mlsanity.com/paper/2503.17811/) (2025)
- [SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probes](https://mlsanity.com/paper/2506.07245/) (2025)
- [CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation](https://mlsanity.com/paper/2507.06013/) (2025)

## Abstract (arXiv)

To tackle the challenges of large language model performance in natural language to SQL tasks, we introduce XiYan-SQL, an innovative framework that employs a multi-generator ensemble strategy to improve candidate generation. We introduce M-Schema, a semi-structured schema representation method designed to enhance the understanding of database structures. To enhance the quality and diversity of generated candidate SQL queries, XiYan-SQL integrates the significant potential of in-context learning (ICL) with the precise control of supervised fine-tuning. On one hand, we propose a series of training strategies to fine-tune models to generate high-quality candidates with diverse preferences. On the other hand, we implement the ICL approach with an example selection method based on named entity recognition to prevent overemphasis on entities. The refiner optimizes each candidate by correcting logical or syntactical errors. To address the challenge of identifying the best candidate, we fine-tune a selection model to distinguish nuances of candidate SQL queries. The experimental results on multiple dialect datasets demonstrate the robustness of XiYan-SQL in addressing challenges across different scenarios. Overall, our proposed XiYan-SQL achieves the state-of-the-art execution accuracy of 75.63% on Bird benchmark, 89.65% on the Spider test set, 69.86% on SQL-Eval, 41.20% on NL2GQL. The proposed framework not only enhances the quality and diversity of SQL queries but also outperforms previous methods.