CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning

This repository contains models and related information for the paper CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning.

Abstract

Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have notable limitations: Self-Consistency may select suboptimal outputs despite majority votes, while Self-Correction typically addresses only syntactic errors. To leverage the strengths of both approaches, we propose CSC-SQL, a novel method that integrates Self-Consistency and Self-Correction. CSC-SQL selects the two most frequently occurring outputs from parallel sampling and feeds them into a merge revision model for correction. Additionally, we employ the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models via reinforcement learning, significantly enhancing output quality. Experimental results confirm the effectiveness and generalizability of CSC-SQL. On the BIRD private test set, our 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.

Code & Resources

• GitHub Repository: https://github.com/CycloneBoy/csc_sql
• Hugging Face Collection: https://huggingface.co/collections/cycloneboy/csc-sql-6835c4a52da10c54bbe14f8e
• ModelScope Collection: https://modelscope.cn/collections/CSC-SQL-8542177708b643

Framework Overview

Main Results

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

Models and Datasets on Hugging Face

The following models and datasets related to CSC-SQL are available on Hugging Face:

Usage

This model can be loaded using the transformers library. Below is an example of how to use the model for text-to-SQL generation. For more detailed instructions on training and evaluation, please refer to the official GitHub repository.

from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load the model and tokenizer model_id = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct" # Example 7B model from the project tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype="auto", # or torch.bfloat16 if supported trust_remote_code=True # Required for custom architectures like Qwen2 ).eval() # Prepare your input: natural language question and database schema question = "What is the average age of students?" schema_info = """ CREATE TABLE students (  student_id INT PRIMARY KEY,  name TEXT,  age INT,  major TEXT ); """ # Replace with actual schema from your database # Construct the prompt using the Qwen2 chat template format # The model expects a structured input that includes the schema and question, followed by "SQL:" formatted_prompt = f"Given the following database schema: {schema_info}  Generate a SQL query for the following natural language question: {question} SQL:" messages = [ {"role": "user", "content": formatted_prompt} ] # Apply the chat template and tokenize text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True # Adds '<|im_start|>assistant ' to prepare for model's response ) inputs = tokenizer(text, return_tensors="pt").to(model.device) # Generate the SQL query generated_ids = model.generate( **inputs, max_new_tokens=256, do_sample=False, # Use greedy decoding for reproducibility temperature=0.7, top_p=0.9, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, ) # Decode and print the generated SQL # Note: The output may contain the original prompt and special tokens. Post-processing might be needed. output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) print(output_text) 

Citation

If you find our work helpful or inspiring, please feel free to cite it:

@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}, }