Detecting structural similarity between queries is essential for selecting examples in in-context learning models. However, assessing structural similarity based solely on the natural language expressions of queries, without considering SQL queries, presents a significant challenge. This paper explores the significance of this similarity metric and proposes a model for accurately estimating it. To achieve this, we leverage a dataset comprising 170k question pairs, meticulously curated to train a similarity prediction model. Our comprehensive evaluation demonstrates that the proposed model adeptly captures the structural similarity between questions, as evidenced by improvements in Kendall-Tau distance and precision@k metrics. Notably, our model outperforms strong competitive embedding models from OpenAI and Cohere. Furthermore, compared to these competitive models, our proposed encoder enhances the downstream performance of NL2SQL models in 1-shot in-context learning scenarios by 1-2\% for GPT-3.5-turbo, 4-8\% for CodeLlama-7B, and 2-3\% for CodeLlama-13B.
@article{arxiv.2403.16204,
title = {SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder},
author = {Mohammadreza Pourreza and Davood Rafiei and Yuxi Feng and Raymond Li and Zhenan Fan and Weiwei Zhang},
journal= {arXiv preprint arXiv:2403.16204},
year = {2024}
}