📖Arxiv Paper | 🤗HuggingFace | 🤖ModelScope |

• August 12, 2025: The SLM-SQL prediction results on the BIRD Dev dataset.
• August 1, 2025: The SLM-SQL 1.5B model achieved an Execution Accuracy (EX) of 70.49% on the BIRD test set, while the 0.5B model attained an EX of 61.82%.
• July 31, 2025: Upload model to modelscope and huggingface.
• July 30, 2025: Publish the paper to arxiv

Large language models (LLMs) have demonstrated strong performance in translating natural language questions into SQL queries (Text-to-SQL). In contrast, small language models (SLMs) ranging from 0.5B to 1.5B parameters currently underperform on Text-to-SQL tasks due to their limited logical reasoning capabilities. However, SLMs offer inherent advantages in inference speed and suitability for edge deployment. To explore their potential in Text-to-SQL applications, we leverage recent advancements in post-training techniques. Specifically, we used the open-source SynSQL-2.5M dataset to construct two derived datasets: SynSQL-Think-916K for SQL generation and SynSQL-Merge-Think-310K for SQL merge revision. We then applied supervised fine-tuning and reinforcement learning-based post-training to the SLM, followed by inference using a corrective self-consistency approach. Experimental results validate the effectiveness and generalizability of our method, SLM-SQL. On the BIRD development set, the five evaluated models achieved an average improvement of 31.4 points. Notably, the 0.5B model reached 56.87% execution accuracy (EX), while the 1.5B model achieved 67.08% EX. We will release our dataset, model, and code to github: https://github.com/CycloneBoy/slm_sql.

slmsql_framework

slm_sql_result

slmsql_bird_main

slmsql_spider_main

Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset.

slmsql_ablation_study

• Release inference code
• Upload Model
• Release training code
• Fix bug
• Update doc

• csc_sql
• open-r1
• OmniSQL

@misc{sheng2025slmsqlexplorationsmalllanguage,
 title={SLM-SQL: An Exploration of Small Language Models for Text-to-SQL}, 
 author={Lei Sheng and Shuai-Shuai Xu},
 year={2025},
 eprint={2507.22478},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 url={https://arxiv.org/abs/2507.22478}, 
}
@misc{sheng2025cscsqlcorrectiveselfconsistencytexttosql,
 title={CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning}, 
 author={Lei Sheng and Shuai-Shuai Xu},
 year={2025},
 eprint={2505.13271},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 url={https://arxiv.org/abs/2505.13271}, 
}