SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation

Abstract

Text-to-SQL enables non-experts to retrieve data from databases by converting natural language queries into SQL. However, state-of-the-art text-to-SQL studies rely on the BIRD dataset, which assumes that evidence is provided along with questions. Although BIRD facilitates research advancements, it assumes that users have expertise and domain knowledge, contradicting the fundamental goal of text-to-SQL. In addition, human-generated evidence in BIRD contains defects, including missing or erroneous evidence, which affects model performance. To address this issue, we propose SEED (System for Evidence Extraction and Domain knowledge generation), an approach that automatically generates evidence to improve performance and practical usability in real-world scenarios. SEED systematically analyzes database schema, description files, and values to extract relevant information. We evaluated SEED on BIRD and Spider, demonstrating that it significantly improves SQL generation accuracy in the no-evidence scenario, and in some cases, even outperforms the setting where BIRD evidence is provided. Our results highlight that SEED-generated evidence not only bridges the gap between research and real-world deployment but also improves the adaptability and robustness of text-to-SQL models. Our code is available at https://github.com/felix01189/SEED

References in corpus (11)

• Sequence to Sequence Learning with Neural Networks
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
• DeepSeek-V3 Technical Report
• The Dawn of Natural Language to SQL: Are We Fully Ready?
• A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
• C3: Zero-shot Text-to-SQL with ChatGPT
• CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL
• A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
• The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models
• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation