Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models
<|reference_start|>Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models: Despite the success of large language models (LLMs) in Text-to-SQL tasks, open-source LLMs encounter challenges in contextual understanding and response coherence. To tackle these issues, we present \ours, a systematic methodology tailored for Text-to-SQL with open-source LLMs. Our contributions include a comprehensive evaluation of open-source LLMs in Text-to-SQL tasks, the \openprompt strategy for effective question representation, and novel strategies for supervised fine-tuning. We explore the benefits of Chain-of-Thought in step-by-step inference and propose the \openexample method for enhanced few-shot learning. Additionally, we introduce token-efficient techniques, such as \textbf{Variable-length Open DB Schema}, \textbf{Target Column Truncation}, and \textbf{Example Column Truncation}, addressing challenges in large-scale databases. Our findings emphasize the need for further investigation into the impact of supervised fine-tuning on contextual learning capabilities. Remarkably, our method significantly improved Llama2-7B from 2.54\% to 41.04\% and Code Llama-7B from 14.54\% to 48.24\% on the BIRD-Dev dataset. Notably, the performance of Code Llama-7B surpassed GPT-4 (46.35\%) on the BIRD-Dev dataset.<|reference_end|>
@article{chen2024open-sql,
    title={Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language
  Models},
    author={Xiaojun Chen and Tianle Wang and Tianhao Qiu and Jianbin Qin and Min
  Yang},
    journal={arXiv preprint arXiv:2405.06674},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06674},
    primaryClass={cs.CL cs.AI}
}