Taming SQL Complexity: LLM-Based Equivalence Evaluation for Text-to-SQL
By: Qingyun Zeng , Simin Ma , Arash Niknafs and more
Potential Business Impact:
Helps computers check if their answers to questions are correct.
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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