E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL

2024-09-25

arXiv.org

AI Summary

Natural language-to-SQL generation faces accuracy bottlenecks due to complex database schemas, ambiguous user intents, and semantic ambiguities. Method: This paper proposes a lightweight, efficient question-augmentation paradigm enabling end-to-end direct schema linking. It explicitly injects schema elements—including tables, columns, values, and conditions—into both the natural language question and SQL generation process; introduces a candidate-predicate augmentation mechanism to enhance semantic alignment for complex queries; and integrates zero-shot, single-turn prompting with large language models (e.g., DeepSeek-Coder-7B-Instruct), combining schema-aware question rewriting and predicate validation. Results: The approach achieves 66.29% execution accuracy on the BIRD benchmark and 56.45% even with small models without fine-tuning—demonstrating that question augmentation substantially improves LLM generalization in text-to-SQL tasks.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Translating Natural Language Queries into Structured Query Language (Text-to-SQL or NLQ-to-SQL) is a critical task extensively studied by both the natural language processing and database communities, aimed at providing a natural language interface to databases (NLIDB) and lowering the barrier for non-experts. Despite recent advancements made through the use of Large Language Models (LLMs), significant challenges remain. These include handling complex database schemas, resolving ambiguity in user queries, and generating SQL queries with intricate structures that accurately reflect the user's intent. In this work, we introduce E-SQL, a novel pipeline specifically designed to address these challenges through direct schema linking and candidate predicate augmentation. E-SQL enhances the natural language query by incorporating relevant database items (i.e., tables, columns, and values) and conditions directly into the question and SQL construction plan, bridging the gap between the query and the database structure. The pipeline leverages candidate predicate augmentation to mitigate erroneous or incomplete predicates in generated SQLs. Comprehensive evaluations on the BIRD benchmark illustrate that E-SQL achieves competitive performance, particularly excelling in complex queries with a 66.29% execution accuracy on the test set. A further observation from our experiments reveals that incorporating schema filtering into the translation pipeline does not have a positive impact on performance when the most advanced proprietary LLMs are used. Additionally, our experiments with small LLMs highlight the importance and positive impact of enriched questions on their performance. Without fine-tuning, single-prompt SQL generation using enriched questions with DeepSeek Coder 7B Instruct 1.5v achieves 56.45% execution accuracy on the BIRD development set.

Problem

• Natural Language Processing
• Database Querying
• Complex SQL Generation

Innovation

• E-SQL
• Enhanced Detail Association
• Mid-complexity SQL Accuracy