arXiv:2504.02327v2 Announce Type: replace Abstract: Natural Language to SQL (NL2SQL) aims to translate natural language queries into executable SQL statements, offering non-expert users intuitive access to databases. While recent approaches leveraging large-scale private LLMs such as GPT-4 have achieved state-of-the-art results, they face two critical challenges: the lack of openness and reproducibility, and the prohibitive computational cost of test-time scaling. To address these issues, we explore improving the model-level performance of small-scale public LLMs in NL2SQL under resource-const
The proliferation of advanced LLMs highlights the need for open-source, resource-efficient alternatives to proprietary models for specialized tasks like NL2SQL.
This research addresses the high computational cost and lack of transparency associated with large private LLMs, pushing towards more accessible and reproducible AI development.
The focus is shifting towards improving smaller, public LLMs for specific tasks, potentially reducing dependency on dominant private models and lowering barriers to entry.
- · Open-source AI developers
- · Small to medium enterprises
- · Database users
- · Academic researchers
- · Proprietary large LLM providers
- · Cloud computing providers (for 'pay-per-query' models)
Improved performance and accessibility of open-source LLMs for NL2SQL tasks.
Reduced operational costs for data access and analysis in organizations utilizing these models.
Accelerated development of domain-specific AI applications built on open-source, efficient models, fostering a more diverse AI ecosystem.
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