arXiv:2606.23693v1 Announce Type: new Abstract: Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct and incorrect clauses equally. This coarse-grained reward design leads to insufficient learning signals for correct SQL generation. To address this issue, we propose EXPO-SQL (EXecution-based
The rapid adoption of Large Language Models (LLMs) in data interaction necessitates more refined methods for accurate natural language processing tasks like Text-to-SQL.
Improved Text-to-SQL capabilities make database interaction more accessible, reducing the need for specialized SQL knowledge and accelerating data analysis workflows.
The ability to generate more accurate SQL queries directly from natural language using granular reinforcement learning will significantly enhance user-database interfaces and LLM applications.
- · Database management systems
- · LLM developers
- · Data analysts
- · SaaS companies
- · Tasks requiring manual SQL query writing
- · Outdated Text-to-SQL methods
More efficient and accurate data querying will become widespread, democratizing access to information within organizations.
This could lead to a reduction in demand for entry-level data engineers and SQL specialists, shifting focus to more complex data architecture roles.
The enhanced integration of natural language with databases could enable more sophisticated autonomous AI agents to interact directly with structured information at scale.
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Read at arXiv cs.CL