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

arXiv: https://arxiv.org/abs/2506.09359 · HTML: https://mlsanity.com/paper/2506.09359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [Calibrating LLM-Based Evaluator](https://mlsanity.com/paper/2309.13308/) (2023)
- [Hybrid Ranking Network for Text-to-SQL](https://mlsanity.com/paper/2008.04759/) (2020)
- [HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition](https://mlsanity.com/paper/2402.15754/) (2024)
- [Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching](https://mlsanity.com/paper/2406.06326/) (2024)
- [SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging](https://mlsanity.com/paper/2408.12733/) (2024)
- [LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?](https://mlsanity.com/paper/2312.10321/) (2023)
- [SQL-Encoder: Improving NL2SQL In-Context Learning Through a Context-Aware Encoder](https://mlsanity.com/paper/2403.16204/) (2024)
- [ETM: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models](https://mlsanity.com/paper/2407.07313/) (2024)
- [Quantifying Semantic Query Similarity for Automated Linear SQL Grading: A Graph-based Approach](https://mlsanity.com/paper/2403.14441/) (2024)

## Built on by (most cited first, 2 of 2)

- [Asynchronous Verified Semantic Caching for Tiered LLM Architectures](https://mlsanity.com/paper/2602.13165/) (2026)
- [Auditing and Repairing LLM-as-Judge Failures in a Production Text-to-SQL Pipeline](https://mlsanity.com/paper/2609.30290/) (2026)

## Abstract (arXiv)

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.