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

Qingyun Zeng; Simin Ma; Arash Niknafs; Ashish Basran; Carol Szabo

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arXiv · 2506.09359

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

Qingyun Zeng·Simin Ma·Arash Niknafs·Ashish Basran·Carol Szabo

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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.

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Qingyun Zeng, Simin Ma, Arash Niknafs, Ashish Basran, Carol Szabo. 2025-06-11. Taming SQL Complexity: LLM-Based Equivalence Evaluation for Text-to-SQL. https://arxiv.org/abs/2506.09359

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