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CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL

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arxiv 2410.01943 v1 pith:7ID2LFJT submitted 2024-10-02 cs.LG cs.AIcs.CLcs.DB

Mohammadreza Pourreza , Hailong Li , Ruoxi Sun , Yeounoh Chung , Shayan Talaei , Gaurav Tarlok Kakkar , Yu Gan , Amin Saberi
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Fatma Ozcan Sercan O. Arik
This is my paper
classification cs.LGcs.AIcs.CLcs.DB
keywords chase-sqlselectioncandidateexecutiontext-to-sqlcandidatesframeworkgeneration
verification ladder T0 review T1 audit T2 compute T3 formal
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In tackling the challenges of large language model (LLM) performance for Text-to-SQL tasks, we introduce CHASE-SQL, a new framework that employs innovative strategies, using test-time compute in multi-agent modeling to improve candidate generation and selection. CHASE-SQL leverages LLMs' intrinsic knowledge to generate diverse and high-quality SQL candidates using different LLM generators with: (1) a divide-and-conquer method that decomposes complex queries into manageable sub-queries in a single LLM call; (2) chain-of-thought reasoning based on query execution plans, reflecting the steps a database engine takes during execution; and (3) a unique instance-aware synthetic example generation technique, which offers specific few-shot demonstrations tailored to test questions.To identify the best candidate, a selection agent is employed to rank the candidates through pairwise comparisons with a fine-tuned binary-candidates selection LLM. This selection approach has been demonstrated to be more robust over alternatives. The proposed generators-selector framework not only enhances the quality and diversity of SQL queries but also outperforms previous methods. Overall, our proposed CHASE-SQL achieves the state-of-the-art execution accuracy of 73.0% and 73.01% on the test set and development set of the notable BIRD Text-to-SQL dataset benchmark, rendering CHASE-SQL the top submission of the leaderboard (at the time of paper submission).

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Cited by 17 Pith papers

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