README.md

Knowledge-to-SQL

[2024/10] Check our video presentation in Underline!

[2024/08] The video presentation of our paper will be available soon.

[2024/08] The presentation of our paper are scheduled at Virtual Poster Session 2, check the poster and slides here.

[2024/05] Our paper is accepted as a findings paper in ACL2024!

We propose a novel framework Knowledge-to-SQL that leverages Data Expert Large Language Model (DELLM) to enhance SQL generation, the paper is available here.

Setup

Environment

The GPU resources we use in our study is 4*A800-SXM4-80G with the corresponding CUDA version 12.1, we strongly recommend using the torch version above 2.0.

# Clone the repository
git https://github.com/Rcrossmeister/Knowledge-to-SQL.git
cd ./Knowledge-to-SQL

# Create the conda environment
conda create -n dellm python=3.11.3
conda activate dellm

# Install the required packages
pip install -r requirements.txt

Dataset

We mainly focus on BIRD dataset in our study, we also support Spider dataset for robustness study.

Training

The training implementaion was inspired by LLaMA Factory, you can check their technical report here.

Quick Start

We provide a script to quick start upon BIRD dataset

Citation

Please cite our paper if you include Knowledge-to-SQL in your work:

@inproceedings{hong2024knowledge,
    title = "Knowledge-to-{SQL}: Enhancing {SQL} Generation with Data Expert {LLM}",
    author = "Hong, Zijin  and
      Yuan, Zheng  and
      Chen, Hao  and
      Zhang, Qinggang  and
      Huang, Feiran  and
      Huang, Xiao",
    booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
    year = "2024"
}