Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL

• URL: http://arxiv.org/abs/2412.12522v1
• Date: Tue, 17 Dec 2024 04:22:22 GMT
• Title: Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL
• Authors: Geling Liu, Yunzhi Tan, Ruichao Zhong, Yuanzhen Xie, Lingchen Zhao, Qian Wang, Bo Hu, Zang Li,
• Abstract summary: Large language models (LLMs) have significantly improved the performance of text-to- systems.<n>Many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness.
• Score: 13.122218546167463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is notably compromised when faced with adversarial perturbations. To address this challenge, we propose a robust text-to-SQL solution, called Solid-SQL, designed to integrate with various LLMs. We focus on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, we design a two-round, structural similarity-based example retrieval strategy for in-context learning. Our method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks, respectively. Furthermore, experimental results show that Solid-SQL delivers an average improvement of 11.6% compared to baselines on the perturbed Spider-Syn, Spider-Realistic, and Dr. Spider benchmarks.

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