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LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?

Fuheng Zhao, Jiayue Chen, Lawrence Lim, Ishtiyaque Ahmad, Divyakant Agrawal, Amr El Abbadi

TL;DR

The paper investigates whether large language models can determine SQL equivalence under semantic and pragmatic notions. It introduces two prompting strategies, Miniature & Mull for semantic tests via simple database counterexamples, and Explain & Compare for pragmatic tests through explanation and comparison, applying them to GPT-3.5 Turbo and GPT-4o across Cosette, Zhong 2020, and Spider datasets. Results show LLMs can effectively help identify semantically equivalent and non-equivalent queries and align well with human judgments for pragmatic similarity, though execution-based metrics remain fallible. The work argues for adopting LLM-SQL-Solver as a privacy-preserving, scalable evaluation metric for NL2SQL and SQL rewriting tasks, while outlining limitations and future directions to reduce hallucinations and broaden datasets.

Abstract

Judging the equivalence between two SQL queries is a fundamental problem with many practical applications in data management and SQL generation (i.e., evaluating the quality of generated SQL queries in text-to-SQL task). While the research community has reasoned about SQL equivalence for decades, it poses considerable difficulties and no complete solutions exist. Recently, Large Language Models (LLMs) have shown strong reasoning capability in conversation, question answering and solving mathematics challenges. In this paper, we study if LLMs can be used to determine the equivalence between SQL queries under two notions of SQL equivalence (semantic equivalence and relaxed equivalence). To assist LLMs in generating high quality responses, we present two prompting techniques: Miniature & Mull and Explain & Compare. The former technique is used to evaluate the semantic equivalence in which it asks LLMs to execute a query on a simple database instance and then explore if a counterexample exists by modifying the database. The latter technique is used to evaluate the relaxed equivalence in which it asks LLMs to explain the queries and then compare if they contain significant logical differences. Our experiments demonstrate using our techniques, LLMs is a promising tool to help data engineers in writing semantically equivalent SQL queries, however challenges still persist, and is a better metric for evaluating SQL generation than the popular execution accuracy.

LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?

TL;DR

The paper investigates whether large language models can determine SQL equivalence under semantic and pragmatic notions. It introduces two prompting strategies, Miniature & Mull for semantic tests via simple database counterexamples, and Explain & Compare for pragmatic tests through explanation and comparison, applying them to GPT-3.5 Turbo and GPT-4o across Cosette, Zhong 2020, and Spider datasets. Results show LLMs can effectively help identify semantically equivalent and non-equivalent queries and align well with human judgments for pragmatic similarity, though execution-based metrics remain fallible. The work argues for adopting LLM-SQL-Solver as a privacy-preserving, scalable evaluation metric for NL2SQL and SQL rewriting tasks, while outlining limitations and future directions to reduce hallucinations and broaden datasets.

Abstract

Judging the equivalence between two SQL queries is a fundamental problem with many practical applications in data management and SQL generation (i.e., evaluating the quality of generated SQL queries in text-to-SQL task). While the research community has reasoned about SQL equivalence for decades, it poses considerable difficulties and no complete solutions exist. Recently, Large Language Models (LLMs) have shown strong reasoning capability in conversation, question answering and solving mathematics challenges. In this paper, we study if LLMs can be used to determine the equivalence between SQL queries under two notions of SQL equivalence (semantic equivalence and relaxed equivalence). To assist LLMs in generating high quality responses, we present two prompting techniques: Miniature & Mull and Explain & Compare. The former technique is used to evaluate the semantic equivalence in which it asks LLMs to execute a query on a simple database instance and then explore if a counterexample exists by modifying the database. The latter technique is used to evaluate the relaxed equivalence in which it asks LLMs to explain the queries and then compare if they contain significant logical differences. Our experiments demonstrate using our techniques, LLMs is a promising tool to help data engineers in writing semantically equivalent SQL queries, however challenges still persist, and is a better metric for evaluating SQL generation than the popular execution accuracy.
Paper Structure (16 sections, 2 figures, 4 tables)

This paper contains 16 sections, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Motivations
  4. Two Notions of SQL Equivalence
  5. Large Language Models Basics
  6. Proposed Method
  7. Evaluations
  8. Data Source
  9. Judging Semantic Equivalence
  10. Judging NL2SQL Generation
  11. Agreement between LLMs' Judgements and Execution Accuracy
  12. Human Evaluation on Samples
  13. Discussion and Future Works
  14. Applications of LLM-SQL-Solver
  15. Future Plans
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Demonstration of Chain-of-Thought, Miniature & MULL, and Explain & Compare prompts. The used SQL queries are not semantically equivalent but are pragmatic equivalent.
  • Figure 2: The rates of LLM correctly executed the SQL queries on its own provided simple database.

Theorems & Definitions (2)

  • Definition 2.1: Semantic Equivalence codd1970relational
  • Definition 2.2: Pragmatic Equivalence baker2018translation