# XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL

arXiv: https://arxiv.org/abs/2507.04701 · HTML: https://mlsanity.com/paper/2507.04701/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 50 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [CodeS: Towards Building Open-source Language Models for Text-to-SQL](https://mlsanity.com/paper/2402.16347/) (2024)
- [GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing](https://mlsanity.com/paper/2009.13845/) (2020)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [C3: Zero-shot Text-to-SQL with ChatGPT](https://mlsanity.com/paper/2307.07306/) (2023)
- [MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL](https://mlsanity.com/paper/2312.11242/) (2023)
- [CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL](https://mlsanity.com/paper/2410.01943/) (2024)
- [MCS-SQL: Leveraging Multiple Prompts and Multiple-Choice Selection For Text-to-SQL Generation](https://mlsanity.com/paper/2405.07467/) (2024)
- [The Dawn of Natural Language to SQL: Are We Fully Ready?](https://mlsanity.com/paper/2406.01265/) (2024)
- [The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models](https://mlsanity.com/paper/2408.07702/) (2024)

## Built on by (most cited first, 30 of 40)

- [Agentar-Scale-SQL: Advancing Text-to-SQL through Orchestrated Test-Time Scaling](https://mlsanity.com/paper/2509.24403/) (2025)
- [BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions](https://mlsanity.com/paper/2510.05318/) (2025)
- [MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL](https://mlsanity.com/paper/2511.01008/) (2025)
- [Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation](https://mlsanity.com/paper/2511.21402/) (2025)
- [A State-of-the-Art SQL Reasoning Model using RLVR](https://mlsanity.com/paper/2509.21459/) (2025)
- [PV-SQL: Synergizing Database Probing and Rule-based Verification for Text-to-SQL Agents](https://mlsanity.com/paper/2604.17653/) (2026)
- [FlexSQL: Flexible Exploration and Execution Make Better Text-to-SQL Agents](https://mlsanity.com/paper/2605.02815/) (2026)
- [RoboPhD: Self-Improving Text-to-SQL Through Autonomous Agent Evolution](https://mlsanity.com/paper/2601.01126/) (2026)
- [Cost Trade-offs of Reasoning and Non-Reasoning Large Language Models in Text-to-SQL](https://mlsanity.com/paper/2512.22364/) (2025)
- [AGRO-SQL: Agentic Group-Relative Optimization with High-Fidelity Data Synthesis](https://mlsanity.com/paper/2512.23366/) (2025)
- [An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data](https://mlsanity.com/paper/2603.07841/) (2026)
- [AgentNLQ: A General-Purpose Agent for Natural Language to SQL](https://mlsanity.com/paper/2605.19010/) (2026)
- [HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance](https://mlsanity.com/paper/2510.08896/) (2025)
- [Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQL](https://mlsanity.com/paper/2511.10674/) (2025)
- [Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering](https://mlsanity.com/paper/2603.20004/) (2026)
- [R3-SQL: Ranking Reward and Resampling for Text-to-SQL](https://mlsanity.com/paper/2604.25325/) (2026)
- [Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards](https://mlsanity.com/paper/2606.06825/) (2026)
- [SQLConductor: Search-to-Policy Learning for Step-wise Text-to-SQL Orchestration](https://mlsanity.com/paper/2606.23537/) (2026)
- [IESR:Efficient MCTS-Based Modular Reasoning for Text-to-SQL with Large Language Models](https://mlsanity.com/paper/2602.05385/) (2026)
- [Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases](https://mlsanity.com/paper/2602.17001/) (2026)
- [SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis](https://mlsanity.com/paper/2604.21414/) (2026)
- [Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2605.03720/) (2026)
- [LEAF-SQL: Level-wise Exploration with Adaptive Fine-graining for Text-to-SQL Skeleton Prediction](https://mlsanity.com/paper/2605.09295/) (2026)
- [Residual Skill Optimization for Text-to-SQL Ensembles](https://mlsanity.com/paper/2605.21792/) (2026)
- [SIRIUS-SQL: Anchoring Multi-Candidate Text-to-SQL in Execution Feedback](https://mlsanity.com/paper/2606.01246/) (2026)
- [CAPER: Clause-Aligned Process Supervision for Text-to-SQL](https://mlsanity.com/paper/2606.03327/) (2026)
- [SOMA-SQL: Resolving Multi-Source Ambiguity in NL-to-SQL via Synthetic Log and Execution Probing](https://mlsanity.com/paper/2606.11424/) (2026)
- [DecoSearch: Complexity-Aware Routing and Plan-Level Repair for Text-to-SQL](https://mlsanity.com/paper/2606.17821/) (2026)
- [Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows](https://mlsanity.com/paper/2607.00828/) (2026)
- [STRATOS: Bridging the Symbolic-to-Numeric Gap in Spatio-Temporal Text-to-SQL for Meteorological Data](https://mlsanity.com/paper/2607.03501/) (2026)

## Abstract (arXiv)

To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates. It consists of three components: 1) a Schema Filter module filtering and obtaining multiple relevant schemas; 2) a multi-generator ensemble approach generating multiple highquality and diverse SQL queries; 3) a selection model with a candidate reorganization strategy implemented to obtain the optimal SQL query. Specifically, for the multi-generator ensemble, we employ a multi-task fine-tuning strategy to enhance the capabilities of SQL generation models for the intrinsic alignment between SQL and text, and construct multiple generation models with distinct generation styles by fine-tuning across different SQL formats. The experimental results and comprehensive analysis demonstrate the effectiveness and robustness of our framework. Overall, XiYan-SQL achieves a new SOTA performance of 75.63% on the notable BIRD benchmark, surpassing all previous methods. It also attains SOTA performance on the Spider test set with an accuracy of 89.65%.