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

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.

@article{zeng2025_2506.09359,
  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 },
  journal={arXiv preprint arXiv:2506.09359},
  year={ 2025 }
}