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About this paper
Wenjia Jiang, Yiwei Wang, Boyan Han, Joey Tianyi Zhou, Chi Zhang
Researchers at Westlake University and collaborators developed SQLAgent, a two-stage framework that enhances Large Language Model (LLM) performance in Text-to-SQL tasks by autonomously acquiring database-specific knowledge before generating queries. This framework achieved 25.78% Execution Accuracy on the Spider 2.0-Snow benchmark, outperforming existing baselines and demonstrating stronger generalization on complex queries.
Key insights
- Decoupling knowledge acquisition from query generation, akin to a human data engineer's workflow, is crucial for improving LLM generalization in Text-to-SQL.
- LLMs can act as both policy and evaluator in an MCTS-like exploration process, effectively building a database-specific knowledge base using high-dimensional natural language feedback.
- A collaborative dual-agent framework can efficiently utilize proactively acquired, database-specific knowledge to iteratively refine SQL generation, enhancing accuracy and robustness for complex reasoning tasks.
Summary by alphaXiv.
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