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

• Prompt-based in-context methods can improve SQL generation, but their performance is constrained by LLM sensitivity to prompt structure and content.The introduction also notes that candidate diversity alone does not ensure correctness or robustness, and self-consistency may not select the optimal query.Source: §I · p. 1
• XiYan-SQL uses a hybrid ensemble of fine-tuned expert models and an in-context-learning model, with a Schema Filter producing diverse schemas for generation.The framework then selects among candidate queries; the introduction describes schema retrieval and iterative column selection.Source: §I · pp. 1–2
• Multi-task fine-tuning targets SQL–text syntactic alignment, while multi-format training encourages semantically equivalent queries with different structures or styles.Examples include complex CTEs versus simpler subqueries and specified stylistic conventions.Source: §I · pp. 1–2
• 75.63% EX on BIRD established XiYan-SQL as the leaderboard’s state of the art, 0.84% ahead of the second-place entry at submission.The paper also reports state-of-the-art 89.65% execution accuracy on the Spider test set.Source: §I · p. 2
• The Schema Filter produces multiple schemas with different precision and recall through multi-path retrieval and iterative column selection.The contribution list describes this as providing varied schema inputs to the downstream generators.Source: §I · pp. 1–2
• A candidate-reorganization strategy and a fine-tuned SQL selection model work together to identify the preferred query from the generated candidates.Candidates are reorganized based on consistency results before the model selects from the structured list.Source: §I · pp. 1–2

• Early Text-to-SQL systems relied on hand-crafted templates, followed by transformer-based encoder–decoder models and pretrained language models such as BERT, TaBERT, and GraPPa [15].The paper says these early methods have limitations in more complex and diverse scenarios.Source: §II · p. 2
• Prompt-engineering approaches use decompositions, demonstrations, chain-of-thought, and multi-agent prompts to guide SQL generation [8] [19].Prior schema-linking methods generally seek one optimized schema for one generator, whereas XiYan-SQL aims to supply multiple complementary schemas.Source: §II · p. 2
• Generating and selecting multiple prompt-based candidates can incur inference overhead and remain sensitive to prompts [8], while fine-tuning methods seek controllable SQL generation with fewer parameters.The paper notes that existing fine-tuning methods can still struggle on challenging scenarios or benchmarks.Source: §II · pp. 2–3
• XiYan-SQL presents a modular ensemble of distinct fine-tuned local models, co-designed with iterative schema filtering to provide diverse inputs and generator styles.The authors position the decoupled components as tools or experts that can complement agent-based systems and describe the framework as a blueprint for robust, private, and cost-effective Text-to-SQL systems.Source: §II · p. 3

A. Overall Framework

• The framework takes a user question, textual evidence, and database schema as inputs, then filters schemas, generates multiple SQL candidates, and selects a query.The selection model uses candidate reorganization to identify the most accurate query.Source: A. Overall Framework · p. 3

B. Schema Filter

• Multi-path retrieval uses question-and-evidence keywords and schema metadata to find relevant tables, columns, and values in large databases.The retrieval stage uses embedding cosine similarity for schema elements and applies edit-distance and embedding-based filtering for values.Source: B. Schema Filter · pp. 3–4
• Iterative column selection generates multiple schemas, preserving primary and foreign keys—including complete composite keys and referenced keys—to maintain join connectivity.As iterations increase, selected schemas tend toward lower precision and higher recall, creating schema diversity for subsequent generation.Source: B. Schema Filter · p. 4

C. Multiple SQL Generation

• The training strategy addresses prompt-format sensitivity by using supervised fine-tuning to target both SQL quality and candidate diversity.The paper motivates this approach by noting that prompt-based generation can make it difficult to ensure quality while increasing diversity.Source: C. Multiple SQL Generation · p. 4
• Joint fine-tuning adds Question Inference, Evidence Inference, and Self-Refine to standard Text-to-SQL training to strengthen language–SQL alignment.The auxiliary tasks infer possible questions from SQL and relevant information, select relevant evidence, and regenerate SQL using prior-generation and execution results.Source: C. Multiple SQL Generation · p. 4, Figure 2 · p. 4
• Multi-format training creates specialized generators using structural variations, such as CTE rewrites, and stylistic variations, such as keyword casing and table aliases.These variations provide different SQL generation styles for the same input.Source: C. Multiple SQL Generation · p. 4, Figure 3 · p. 5
• The generation stage combines multiple fine-tuned generators with an ICL-based model across filtered schemas, producing candidates and retrying generations that encounter execution errors.The candidate count is determined by the number of filtered schemas multiplied by the number of generators.Source: C. Multiple SQL Generation · p. 5

D. SQL Selection

• A selection model chooses among candidates after reorganization based on consistency, addressing cases where self-consistency lacks a majority or favors an incorrect result.The approach pairs the selection model with a candidate reorganization strategy to guide its attention.Source: D. SQL Selection · p. 5
• Selection training labels candidates with identical execution results as positive, while negatives include natural failures and LLM-modified near-miss queries.Positive examples need not match the ground-truth SQL string when their execution result is identical.Source: D. SQL Selection · p. 5
• Training and inference candidates are de-formalized to remove superficial style differences, focusing selection on semantic correctness.A standard SQL formatter removes differences such as casing and spacing.Source: D. SQL Selection · p. 5
• At inference, executable candidates are clustered by execution-result consistency, then ordered by cluster size and generator performance.When a consensus result predominates, candidates are presented in cluster order; otherwise, one candidate is selected from each group for the selection model.Source: D. SQL Selection · p. 5
• The selection model receives the question, union of filtered schemas, evidence, and reorganized candidates; the ordering uses consistency priors while mitigating interference.Candidates are sorted across groups by size and within groups by generator order.Source: D. SQL Selection · pp. 5–6

IV. EXPERIMENTS

• The experiments are designed to demonstrate XiYan-SQL’s state-of-the-art performance on important benchmarks and assess its framework through ablations and component analysis.The evaluation also aims to provide empirical insights into XiYan-SQL.Source: §IV · p. 6
• Comparisons use prior methods’ publicly available benchmark results, which the authors describe as typically their most powerful implementations.Source: §IV · p. 6
• Reproduced comparison results use the same configuration as XiYan-SQL, including base models.Source: §IV · p. 6

A. Experimental Setup

• BIRD evaluation uses its development set locally and submits results on its unreleased test set, while Spider evaluation focuses on its test set.BIRD contains 12,751 question–SQL pairs across 95 databases; Spider contains 10,181 questions across 200 databases.Source: A. Experimental Setup · p. 6
• Execution Accuracy measures whether predicted SQL execution results match those of the ground-truth query, with slight calculation differences between BIRD and Spider.BIRD’s latest version also reports R-VES, which evaluates runtime efficiency for correctly predicted queries alongside accuracy.Source: A. Experimental Setup · p. 6
• The final framework uses Qwen2.5-Coder-32B for five generators, including four fine-tuned models and one ICL-based generator, and Qwen2.5-Coder-7B for selection.GPT-4o and Qwen-Max assist with constructing multi-task and multi-format data; GPT-4o also augments selection training data.Source: A. Experimental Setup · pp. 6–7
• With two filtered schemas and five generators, the implementation produces ten SQL candidates; the authors did not increase the model or candidate count because of cost and resource limits.Source: A. Experimental Setup · pp. 6–7
• Table I presents a performance comparison of Text-to-SQL methods on the BIRD benchmark.Source: Table I · p. 7

B. Main Results

• 75.63% EX on BIRD set a leaderboard SOTA 0.84% above second place, while Spider achieved 89.65% EX SOTA without Spider-specific tuning.The Spider result complements BIRD’s value-heavy complexity with Spider’s structural complexity.Source: B. Main Results · p. 7, Table II · p. 7

C. Ablation Study

• Removing the Schema Filter dropped BIRD dev performance by about 1.24%, while using one generation model instead of multiple caused an approximately 4% decline.Removing the proposed selection method and relying on majority voting also significantly decreased performance.Source: C. Ablation Study · p. 7, Table III · p. 8

D. Study of Schema Filter

• The Schema Filter achieved higher column and value recall than reproduced schema-linking methods while maintaining precision, and iterations adjusted the precision–recall balance to produce diverse schemas.S1 and S2 denote schemas from the first and second filter iterations, respectively.Source: D. Study of Schema Filter · p. 8, Table IV · p. 8
• Across three averaged generators, schema-filter methods improved final-query results over the full-schema method, with the first iteration performing better than the second.The comparison reports ΔEX relative to the full-schema method.Source: D. Study of Schema Filter · p. 8, Table V · p. 8

E. Study of Single SQL Generation

• Multi-task training improved single-generator results by 8.3%–15.3% across model types and sizes; Qwen2.5-Coder-32B reached 66.88% EX.Against conventional fine-tuning, reported gains across five basic fine-tuned models were 4.5%, 4.04%, 2.9%, 2.9%, and 2.2%.Source: E. Study of Single SQL Generation · p. 8
• Removing Question Inference caused the largest auxiliary-task ablation drop; removing Self-refine or Evidence Inference also degraded performance, while inference-time self-refinement added about 1%.The full multi-task model outperformed the leave-one-out variants and the primary-task-only baseline.Source: E. Study of Single SQL Generation · pp. 8–9, Table VII · p. 9

F. Study of Multiple SQL Generation

• Multi-FT balanced candidate diversity and accuracy, improving accuracy by about 2% over ICL-Prompt; adding ICL generation to fine-tuned models further enhanced diversity while maintaining quality.Figure 4(a) compares methods using five candidates.Source: F. Study of Multiple SQL Generation · p. 9, Figure 4 · p. 10
• With 10 candidates, the multi-generator pool’s upper-bound reached 82.2%, while its lower-bound decreased; multi-task training supports generator quality and multi-format training induces diversity.The upper-bound reflects potential candidate correctness, making selection more consequential as the candidate set grows.Source: F. Study of Multiple SQL Generation · p. 9, Figure 4 · p. 10

G. Study of SQL Selection

• The reorganized selection strategy outperformed majority voting by about 3% from its 69.82% EX, and the fine-tuned selector beat the baseline and advanced out-of-the-box models.The strategy organizes candidates to focus the selection model’s attention; its advantage becomes more pronounced as candidate counts increase.Source: G. Study of SQL Selection · pp. 9–10, Table VIII · p. 10

H. Analysis of Results from Different SQL Generators

• Contribution Ratio measures which generator supplied the selected candidate among non-unanimous cases; about 45% of cases were excluded because all candidates had identical execution results.The contribution ratios sum to 100%.Source: H. Analysis of Results from Different… · p. 10
• SQLG1 had the best individual Avg. EX and highest contribution, while specialized SQLG2–SQLG4 traded some peak accuracy for complementary generations on challenging cases.SQLG2 favors structural variation, SQLG3 stylistic variation, SQLG4 combines multi-task and multi-format data, and SQLG5 uses GPT-4o ICL.Source: H. Analysis of Results from Different… · p. 10, Table X · p. 11
• SQLG2 and SQLG5 showed higher R1 values, indicating unique correct outputs, whereas SQLG3 and SQLG4 had more middle-range distributions and SQLG1 showed higher R3 and R4.Rn denotes cases where only n generators produced correct outputs.Source: H. Analysis of Results from Different… · p. 10
• On a BIRD dev query requiring two reasoning steps, SQLG1’s aggregation failed, but SQLG2’s CTE decomposed the steps into a correct, readable solution.The authors describe SQLG2’s structural diversity as complementary, while noting it is not entirely generalizable.Source: H. Analysis of Results from Different… · p. 11, Figure 5 · p. 11

I. Analysis of Cost Efficiency

• Table XI groups correct BIRD dev answers by how many generators succeeded, distinguishing unique solutions at R1 from universally correct answers at R5.The passage defines Rn as the percentage of correct answers from a generator when exactly n models succeeded.Source: Table XI · p. 11
• 45 GPU hours for the 32B SQL generator and 15 GPU hours for the 7B selection model were reported as training costs.The runs used NVIDIA A100 80G GPUs and consumed about 180 million and 50 million tokens, respectively.Source: §I · p. 11
• 40.81 seconds was the approximate end-to-end latency per BIRD dev sample; executing 10 candidates averaged 0.35 seconds on an Apple M4 Pro.Execution time depends on hardware and database scale; the reported averages include self-refine operations.Source: §I · p. 11
• With 10 candidates, XiYan-SQL surpasses CHASE-SQL’s 21-candidate performance at 60% of its latency.Table XII summarizes average inference costs; the comparison’s latency is based on information provided by CHASE-SQL.Source: §I · p. 11, Table XII · p. 12

J. Study of Generalization

• 2.3% higher execution accuracy on BIRD’s black-box test set than its public dev set exceeded the below-1% margins typical of previous SOTA methods.The authors say this suggests the framework learns generalizable translation patterns rather than merely overfitting to the development set.Source: J. Study of Generalization · p. 12
• 41.0% on BIRD-CRITIC reached SOTA on the public leaderboard, providing an out-of-domain generalization result.The benchmark is described as addressing complex user challenges in real-world applications.Source: J. Study of Generalization · p. 12
• The authors summarize strong BIRD test performance together with BIRD-CRITIC SOTA as evidence of a highly generalizable framework.They describe the framework as ready for real-world application challenges.Source: J. Study of Generalization · p. 12

V. DISCUSSION

• Once schema-linking recall reaches a threshold such as 80%, improving precision becomes more beneficial for the overall result.The authors cite iterations of the Schema Filter as evidence for balancing precision and recall.Source: §V · p. 12
• Relevant multiple tasks can improve fine-tuned SQL generators, and individual generation quality should precede increasing candidate diversity.The task mix may be adjusted based on thematic considerations; the quality-before-diversity principle applies to multiple generators or one generator with multi-path generation.Source: §V · p. 12
• API reasoning models can outperform a standalone fine-tuned model on some tasks, while the full XiYan-SQL pipeline achieved the highest accuracy on two complex internal datasets.The passage notes superior API-model generalization on some SQL-reasoning tasks, such as BIRD-CRITIC’s SQL debug task.Source: §V · p. 12
• LLM SQL selection is sensitive to candidate order and organization, so adding prior knowledge can help enhance performance.The authors report this sensitivity for fine-tuned and non-fine-tuned LLMs, similarly to previous studies [37],.Source: §V · p. 12

VI. CONCLUSION

• XiYan-SQL combines multi-path retrieval and iterative column selection with multi-task, multi-format generators and a reorganized candidate-selection module.The authors state that varied generation styles are important for complex queries where a single style may fail.Source: §VI · p. 12
• Future directions include strengthening specialized-model reasoning and extending XiYan-SQL toward schema linking and query optimization.The latter is framed as a possible unified “all-in-one” model for a broader range of SQL-related tasks.Source: §VI · pp. 12–13