Summary of Solid-sql: Enhanced Schema-linking Based In-context Learning For Robust Text-to-sql, by Geling Liu et al.
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL
by Geling Liu, Yunzhi Tan, Ruichao Zhong, Yuanzhen Xie, Lingchen Zhao, Qian Wang, Bo Hu, Zang Li
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a robust text-to-SQL system called Solid-SQL, designed to integrate with various large language models (LLMs). The authors reveal that while LLM-driven methods excel on standard datasets, their accuracy is compromised when faced with adversarial perturbations. To address this challenge, the team focuses on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, they design a two-round, structural similarity-based example retrieval strategy for in-context learning. The proposed method achieves state-of-the-art SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better computer systems that can understand and execute SQL commands from natural language. Right now, these systems are not very good at handling tricky situations or fake data. The researchers propose a new way to make these systems more robust by using big language models and special techniques to prepare the data. This new approach does really well on standard tests and even better when it comes up against tricky data. It’s an important step forward in making computer systems that can understand and work with natural language. |
Keywords
» Artificial intelligence » Data augmentation
