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

Qingyun Zeng, Simin Ma, Arash Niknafs, Ashish Basran, Carol Szabo
6/11/2025
cs.CL

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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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Cite this paper

@article{zeng2025taming,
  title  = {Taming SQL Complexity: LLM-Based Equivalence Evaluation for Text-to-SQL},
  author = {Qingyun Zeng and Simin Ma and Arash Niknafs and Ashish Basran and Carol Szabo},
  year   = {2025},
  eprint = {2506.09359},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.09359v1}
}

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