DSMR-SQL: Enhancing Text-to-SQL with Dual-Strategy SQL Generation and Multi-Role SQL Selection

ACL ARR 2025 February Submission1971 Authors

Abstract: Recent advancements in Large Language Models (LLMs) have markedly improved SQL generation. Nevertheless, existing approaches typically rely on single-model designs, limiting their capacity to effectively handle complex user queries. In addition, current methods often face difficulties in selecting the optimal SQL from multiple candidates. To mitigate these limitations, this study presents DSMR-SQL, a two-stage framework consisting of: (1) Dual-Strategy SQL Generation: DSMR-SQL aims to produce a broader spectrum of SQL queries by using multiple models with two strategies: Supervised Fine-Tuning and In-Context Learning; (2) Multi-Role SQL Selection: DSMR-SQL seeks to identify the SQL most aligning with user intent by introducing a collaborative framework involving three roles (i.e., Proposer, Critic, Summarizer). Extensive experiments on various datasets substantiate the efficacy of DSMR-SQL in enhancing SQL generation.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: code generation and understanding

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data analysis

Languages Studied: English

Submission Number: 1971