arXiv:2606.17821v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in translating natural language to SQL, yet existing methods still falter on complex queries requiring multi-step, data-aware reasoning. We introduce DecoSearch, a training-free framework that addresses this by routing each query to the appropriate level of reasoning effort. A lightweight Schema Selector first prunes the full database schema to the relevant tables and columns. An LLM Judger then decides whether the question requires decomposition: straightforward questions fol
The rapid advancement of Large Language Models (LLMs) has highlighted their current limitations in complex, multi-step reasoning, making this an opportune moment for innovative solutions to practical challenges like Text-to-SQL.
This development is crucial for strategic readers as it improves the reliability and complexity handling of LLMs in critical data interaction tasks, expanding their utility and reducing the need for human intermediaries in data querying.
The introduction of DecoSearch changes the paradigm for Text-to-SQL by intelligently routing and repairing queries, enabling LLMs to tackle previously intractable complex data questions more effectively and autonomously.
- · AI developers
- · Data-intensive industries
- · Software engineers
- · LLM providers
- · Manual SQL coders for complex queries
- · Companies with suboptimal database interfaces
Improvements in Text-to-SQL accuracy will lead to broader adoption of LLM-driven data analytics and business intelligence tools.
Increased efficiency in data querying could accelerate development cycles and empower non-technical users to access and analyze complex datasets directly.
The enhanced autonomous data interaction capabilities of LLMs could further accelerate the evolution of fully autonomous AI agents across various enterprise systems.
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Read at arXiv cs.AI