Taming SQL Complexity: LLM-Based Equivalence Evaluation for Text-to-SQL
It addresses a key evaluation problem for developers and users of Text-to-SQL systems, but appears incremental as it builds on existing LLM-based approaches.
This paper tackles the challenge of evaluating semantic equivalence in Text-to-SQL systems by using LLMs to assess both semantic and weak semantic equivalence, analyzing patterns of SQL equivalence and inequivalence.
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.