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Judging the equivalence between two SQL queries is a fundamental problem withmany practical applications in data management and SQL generation (i.e.,evaluating the quality of generated SQL queries in text-to-SQL task). While theresearch community has reasoned about SQL equivalence for decades, it posesconsiderable difficulties and no complete solutions exist. Recently, LargeLanguage Models (LLMs) have shown strong reasoning capability in conversation,question answering and solving mathematics challenges. In this paper, we studyif LLMs can be used to determine the equivalence between SQL queries under twonotions of SQL equivalence (semantic equivalence and relaxed equivalence). Toassist LLMs in generating high quality responses, we present two promptingtechniques: Miniature & Mull and Explain & Compare. The former technique isused to evaluate the semantic equivalence in which it asks LLMs to execute aquery on a simple database instance and then explore if a counterexample existsby modifying the database. The latter technique is used to evaluate the relaxedequivalence in which it asks LLMs to explain the queries and then compare ifthey contain significant logical differences. Our experiments demonstrate usingour techniques, LLMs is a promising tool to help data engineers in writingsemantically equivalent SQL queries, however challenges still persist, and is abetter metric for evaluating SQL generation than the popular executionaccuracy.

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