Taming SQL Complexity: LLM-Based Equivalence Evaluation for Text-to-SQL

TL;DR

This paper investigates using Large Language Models to evaluate the semantic equivalence of generated SQL queries in Text-to-SQL systems, addressing challenges posed by ambiguity and multiple valid interpretations.

Contribution

It introduces LLM-based methods for assessing SQL equivalence, including semantic and weak equivalence, and analyzes common patterns and challenges in this evaluation process.

Findings

LLMs can effectively evaluate semantic equivalence of SQL queries.

Identification of common patterns in SQL equivalence and inequivalence.

Discussion of challenges in LLM-based SQL evaluation.

Abstract

The rise of Large Language Models (LLMs) has significantly advanced Text-to-SQL (NL2SQL) systems, yet evaluating the semantic equivalence of generated SQL remains a challenge, especially given ambiguous user queries and multiple valid SQL interpretations. This paper explores using LLMs to assess both semantic and a more practical "weak" semantic equivalence. We analyze common patterns of SQL equivalence and inequivalence, discuss challenges in LLM-based evaluation.