Can the Rookies Cut the Tough Cookie? Exploring the Use of LLMs for SQL Equivalence Checking

• URL: http://arxiv.org/abs/2412.05561v2
• Date: Sun, 08 Jun 2025 19:02:31 GMT
• Title: Can the Rookies Cut the Tough Cookie? Exploring the Use of LLMs for SQL Equivalence Checking
• Authors: Rajat Singh, Srikanta Bedathur,
• Abstract summary: We introduce a novel, realistic, and sufficiently complex benchmark called SQLEquiQuest for query equivalence checking.<n>We evaluate several state-of-the-art LLMs using various prompting strategies and carefully constructed in-context learning examples.<n>Our analysis shows that LLMs exhibit a strong bias for equivalence predictions, with consistently poor performance over non-equivalent pairs.
• Score: 15.42143912008553
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Equivalence checking of SQL queries is an intractable problem often encountered in settings ranging from grading SQL submissions to debugging query optimizers. Despite recent work toward developing practical solutions, only simple queries written using a small subset of SQL are supported, leaving the equivalence checking of sophisticated SQL queries at the mercy of intensive, potentially error-prone, manual analysis. In this paper, we explore how LLMs can be used to reason with SQL queries to address this challenging problem. Towards this, we introduce a novel, realistic, and sufficiently complex benchmark called SQLEquiQuest for SQL query equivalence checking that reflects real-world settings. We establish strong baselines for SQL equivalence checking by leveraging the ability of LLMs to reason with SQL queries. We conduct a detailed evaluation of several state-of-the-art LLMs using various prompting strategies and carefully constructed in-context learning examples, including logical plans generated by SQL query processors. Our empirical evaluation shows that LLMs go well beyond the current capabilities of formal models for SQL equivalence, going from a mere 30% supported query pairs to full coverage, achieving up to 82% accuracy on Spider+DIN. However, a critical limitation of LLMs revealed by our analysis is that they exhibit a strong bias for equivalence predictions, with consistently poor performance over non-equivalent pairs, opening a new direction for potential future research.

Related papers

• Taming SQL Complexity: LLM-Based Equivalence Evaluation for Text-to-SQL [0.0]
  This paper explores using Large Language Models (LLMs) to assess both semantic and a more practical "weak" semantic equivalence.<n>We analyze common patterns ofsql equivalence and inequivalence, discuss challenges in LLM-based evaluation.
  arXiv  Detail & Related papers  (2025-06-11T03:16:39Z)
• Weaver: Interweaving SQL and LLM for Table Reasoning [63.09519234853953]
  Weaver generates a flexible, step-by-step plan that combinessql for structured data retrieval with LLMs for semantic processing.<n>Weaver consistently outperforms state-of-the-art methods across four TableQA datasets, reducing both API calls and error rates.
  arXiv  Detail & Related papers  (2025-05-25T03:27:37Z)
• Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types [11.391598870596392]
  Large language models (LLMs) have significantly advanced text-to-speech systems. LLMs often narrowly focus on SQL generation, neglecting the complexities of real-world conversational queries. We propose MM, a test suite designed to evaluate the question classification and SQL generation capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-12-21T10:13:45Z)
• PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL [54.304872649870575]
  Large Language Models (LLMs) have emerged as powerful tools for Text-to-sense tasks. In this study, we propose that employing query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type.
  arXiv  Detail & Related papers  (2024-09-21T09:33:14Z)
• RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL [48.516004807486745]
  Large language models (LLMs) with in-context learning have significantly improved the performance of text-to- task. We propose RB-, a novel retrieval-based framework for in-context prompt engineering. Experiment results demonstrate that our model achieves better performance than several competitive baselines on public datasets BIRD and Spider.
  arXiv  Detail & Related papers  (2024-07-11T08:19:58Z)
• CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions [22.493487741249716]
  Large Language Models (LLMs) have been demonstrated to possess impressive capabilities in a variety of domains and tasks. We investigate the issue of prompt design in the multi-turn text-to- task and attempt to enhance the LLMs' reasoning capacity.
  arXiv  Detail & Related papers  (2024-05-04T16:56:14Z)
• PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency [19.067737007347613]
  Methods achieve new SOTA results on the Spider benchmark, with an execution accuracy of 87.6%. Our methods achieve new SOTA results on the Spider benchmark, with an execution accuracy of 87.6%.
  arXiv  Detail & Related papers  (2024-03-13T02:32:41Z)
• MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL [47.120862170230566]
  Recent Text-to-yourself methods usually suffer from significant performance degradation on "huge" databases. We introduce MAC, a novel Text-to-yourself LLM-based multi-agent collaborative framework. In our framework, we leverage GPT-4 as the strong backbone for all agent tasks to determine the upper bound of our framework. We then fine-tune an open-sourced instruction-followed model,sql-Llama, by leveraging Code 7B, to accomplish all tasks as GPT-4 does.
  arXiv  Detail & Related papers  (2023-12-18T14:40:20Z)
• LLM-SQL-Solver: Can LLMs Determine SQL Equivalence? [8.051106281989671]
  Large Language Models (LLMs) have shown strong reasoning capability in conversation, question answering and solving challenges. To assist LLMs in generating high quality responses, we present two prompting techniques: Miniature & Mull and Explain & Compare.
  arXiv  Detail & Related papers  (2023-12-16T05:01:23Z)
• TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning [55.33939289989238]
  We propose TAP4LLM as a versatile pre-processor suite for leveraging large language models (LLMs) in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding.
  arXiv  Detail & Related papers  (2023-12-14T15:37:04Z)
• Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation [76.76046657162306]
  Large language models (LLMs) have emerged as a new paradigm for Text-to- task. Large language models (LLMs) have emerged as a new paradigm for Text-to- task.
  arXiv  Detail & Related papers  (2023-08-29T14:59:54Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• Allies: Prompting Large Language Model with Beam Search [107.38790111856761]
  In this work, we propose a novel method called ALLIES. Given an input query, ALLIES leverages LLMs to iteratively generate new queries related to the original query. By iteratively refining and expanding the scope of the original query, ALLIES captures and utilizes hidden knowledge that may not be directly through retrieval.
  arXiv  Detail & Related papers  (2023-05-24T06:16:44Z)
• Weakly Supervised Text-to-SQL Parsing through Question Decomposition [53.22128541030441]
  We take advantage of the recently proposed question meaning representation called QDMR. Given questions, their QDMR structures (annotated by non-experts or automatically predicted) and the answers, we are able to automatically synthesizesql queries. Our results show that the weakly supervised models perform competitively with those trained on NL- benchmark data.
  arXiv  Detail & Related papers  (2021-12-12T20:02:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.