🇨🇳 中文版

🦅 Falcon: Enterprise-Grade Text-to-SQL Benchmark

A Comprehensive Chinese Text-to-SQL Benchmark for Complex, Cross-Domain Analytical Scenarios

Introduction | Dataset Structure | Getting Started | Citation

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📖 Introduction

Falcon is a continuously evolving, high-quality benchmark designed to bridge the gap between academic Text-to-SQL datasets and real-world enterprise requirements. Unlike traditional benchmarks, Falcon focuses on MaxCompute/Hive dialects and stresses models with complex SQL patterns and linguistic ambiguities common in production environments.

Key Features

• SQL Complexity: Heavy focus on multi-table joins (77% of samples), nested CTEs, window functions, ranking, and type casting.
• Linguistic Challenges: Includes Chinese fuzzy time expressions, colloquial business jargon, ellipsis, and multi-intent questions.
• Enterprise Scale: Schemas involve denormalized fields, implicit foreign keys, and domain-specific synonyms.

The current release is built on curated public datasets covering Finance, Internet, and Retail domains.

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📂 Dataset Structure

To facilitate robust evaluation, the Falcon benchmark is split into a Development Set (with ground truth) and a Test Set (blind).

Repository Layout

FALCON/
├── dev_data/                   # Development Set
│   ├── dev.json                # Questions, SQL, and Execution Results
│   ├── tables.json             # Schema definitions (PK/FK/Columns)
│   └── dev_databases/          # SQLite/CSV source files for execution
│
├── test_data/                  # Test Set
│   ├── test.json               # Questions ONLY (Ground truth hidden)
│   ├── tables.json             # Schema definitions
│   └── test_databases/         # SQLite/CSV source files
│
├── simple_agent/               # [NEW] Lightweight Evaluation Scripts
│   ├── comparator.py           # SQL execution result comparator
│   ├── utils.py                # Utilities for SQL extraction from LLM response
│   └── simple_benchmark.py     # Main script to run dev/test evaluation
│
├── submission/                 # [NEW] Submission Helpers & Examples
│   ├── example_submission_csv/ # Example CSV files for leaderboard submission
│   ├── example_submission_sql/ # Example SQL files for leaderboard submission
│   └── format_submission.py    # Helper to convert DB-GPT Excel output to Zip
│
└── README.md

Data Format Details

1. Development Data (dev_data/dev.json)

Used for few-shot prompting, fine-tuning, or debugging. Contains the natural language question, the ground truth SQL, and the expected execution result.

[
  {
    "question_id": "1",
    "dataset_id": "finance_01",
    "question": "每个性别的平均年龄是多少，按年龄排序？",
    "sql": "SELECT Gender, AVG(Age) FROM customers GROUP BY Gender ORDER BY AVG(Age)",
    "answer": {
      "Gender": ["Female", "Male"],
      "AvgAge": [27.73, 27.84]
    },
    "is_order": "0"
  }
]

2. Test Data (test_data/test.json)

Used for the official leaderboard. Only the question and schema reference are provided.

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🚀 Getting Started

We currently provide two methods for evaluating your models on the Falcon benchmark: a lightweight script-based approach and a GUI-based approach via DB-GPT.

Method 1: Simple Agent (Script-based)

The simple_agent directory contains a lightweight evaluation pipeline. You can use simple_benchmark.py to run evaluations on either the development or test sets.

1. Clone the Repository

   git clone https://github.com/eosphoros-ai/Falcon.git
   cd Falcon

2. Setup Environment Ensure you have the necessary Python dependencies installed.

   pip install openai pandas tqdm

3. Run Evaluation

   • Development Set: Run the benchmark on the dev set to check performance against ground truth.

     cd simple_agent
     python simple_benchmark.py dev

   • Test Set: Run the benchmark on the test set to generate predictions.

     cd simple_agent
     python simple_benchmark.py test

     Note on Submission: After execution, a submission.zip will be automatically generated. For official leaderboard submission, a trace log (we recommend .jsonl format) is required. Please ensure you manually include your trace log in the final ZIP before submitting.

Method 2: DB-GPT (GUI-based)

Falcon is fully integrated into DB-GPT, allowing you to evaluate both Models (LLMs) and Agents through a visual interface.

1. Configuration & Execution Please refer to the official DB-GPT Evaluation Documentation for detailed steps on how to:

   • Import the Falcon benchmark dataset.
   • Configure your Model or Agent.
   • Run the evaluation pipeline via the "Models Evaluation" module.

2. Format Submission DB-GPT will generate an evaluation report in Excel (.xlsx) format. To submit your results to the Falcon leaderboard, you must convert this file into the required ZIP format using our helper script.

   # Run the formatting script
   python submission/format_submission.py --input <path_to_dbgpt_output.xlsx> --output submission.zip

   Note: The generated submission.zip will contain the required result_sql and result_csv folders formatted correctly for the leaderboard.

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📤 Submission

Once you have generated your SQL queries (and execution results), please refer to the submission/ directory for format requirements.

• Examples: Check submission/example_submission_csv and submission/example_submission_sql for the expected file structure.
• Guidelines: Please refer to the Falcon Submission Guidelines for detailed rules.

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📝 Citation

If you use Falcon in your research or development, please cite our paper:

@article{falcon2025,
  title={Falcon: A Comprehensive Chinese Text-to-SQL Benchmark for Enterprise-Grade Evaluation},
  author={Luo, Wenzhen and Guan, Wei and Yao, Yifan and Pan, Yimin and Wang, Feng and Yu, Zhipeng and Wen, Zhe and Chen, Liang and Zhuang, Yihong},
  journal={arXiv preprint arXiv:2510.24762},
  year={2025},
  url={https://arxiv.org/abs/2510.24762}
}

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⚖️ License

This project is licensed under the Apache License, Version 2.0.
See the LICENSE file for the full text.

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_{Maintained by Eosphoros AI}