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OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
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OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
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Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research.
Forward citations
Cited by 11 Pith papers
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ACE-SQL: Adaptive Co-Optimization via Empirical Credit Assignment for Text-to-SQL
ACE-SQL jointly optimizes schema linking and SQL generation via RL with empirical credit assignment from execution-correct rollouts, achieving 65.3% greedy execution accuracy on BIRD Dev using 0.93k output tokens.
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EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL
EXPO-SQL improves Text-to-SQL by using clause-level rewards derived from execution error messages and incremental clause execution instead of uniform query-level rewards.
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SpotIt+: Verification-based Text-to-SQL Evaluation with Database Constraints
SpotIt+ uses verification to find realistic counterexample databases that expose discrepancies between generated and gold SQL queries missed by standard test-based evaluation on the BIRD dataset.
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Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL
An evolving Vulnerability Codex plus hypothesis-driven perturbations exposes latent Text-to-SQL failures in LLMs far better than fixed expert rules, with transferable patterns and early remediation gains.
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Non-vacuous PAC-Bayes generalization bounds for billion-parameter RLVR models, obtained by a Gumbel-max reparameterization and aggressive TinyLoRA distillation/quantization, are claimed for four tasks.
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EcoTable is the first NL-based data integration framework that builds a join-likelihood graph, uses two-stage schema linking and Steiner tree search to find paths, then generates transformations with LLMs, reporting >...
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EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries
Query-driven table integration that uses Steiner-tree search to choose which joins LLMs must verify, reporting 30%+ accuracy gains at 5x lower LLM cost.
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A selection technique based on separating instances and provenance outperforms baselines for choosing among 2-3 NL2SQL candidates on a BIRD-DEV subset without consistency scores.
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FINER-SQL: Boosting Small Language Models for Text-to-SQL
FINER-SQL boosts 3B-parameter small language models to 67.73% and 85% execution accuracy on BIRD and Spider benchmarks via dense memory and atomic rewards in group relative policy optimization, matching larger LLMs at...
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APMPO boosts average Pass@1 scores on math reasoning benchmarks by 3 points over GRPO by using an adaptive power-mean policy objective and feedback-driven clipping bounds in RLVR training.
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Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs
FREIA applies free energy principles and adaptive advantage shaping to unsupervised RL, outperforming baselines by 0.5-3.5 Pass@1 points on math reasoning with a 1.5B model.
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