arXiv:2606.15598v1 Announce Type: new Abstract: Text-to-SQL aims to translate natural language questions into executable SQL queries over structured databases, enabling non-expert users to access data intuitively. While recent advances in large language models (LLMs) have shown promise in this task, existing LLM-based approaches often struggle to strike a balance between strong reasoning capabilities and robust generalization. To address these limitations, we propose CoTE-SQL to enhance the LLM-based text-to-SQL generation with three key innovations: (i) self-enhanced reasoning traces distille
The rapid advancement and widespread deployment of Large Language Models (LLMs) are pushing researchers to address their inherent limitations in practical, domain-specific applications like Text-to-SQL.
Improving the reasoning and generalization capabilities of LLMs in specific tasks like Text-to-SQL expands their utility, reduces the need for highly specialized human database interaction, and makes data more accessible to non-technical users.
This innovation proposes a method to significantly enhance LLM performance in translating natural language to SQL, marking a step towards more robust and generalizable AI agents capable of complex data interaction.
- · LLM developers
- · Enterprises with large databases
- · Data analysts
- · SQL specialists (for routine queries)
- · Traditional BI tools
Increased efficiency and accuracy in database querying through natural language interfaces.
Reduced barriers to data access for business users, leading to faster insights and potentially new data-driven products.
Acceleration of autonomous data agents capable of interacting with and manipulating structured data without human oversight.
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Read at arXiv cs.AI