TL;DR
This paper introduces an automatic method for generating database descriptions to improve Text-to-SQL tasks, employing a dual-process approach that enhances understanding of database schemas and boosts SQL accuracy.
Contribution
It presents a novel dual-process method for automatic database description generation, improving schema understanding and SQL performance in Text-to-SQL tasks.
Findings
Improves SQL accuracy by 0.93% with generated descriptions.
Achieves 37% of human-level performance.
Method is validated on the Bird benchmark.
Abstract
In the context of the Text-to-SQL task, table and column descriptions are crucial for bridging the gap between natural language and database schema. This report proposes a method for automatically generating effective database descriptions when explicit descriptions are unavailable. The proposed method employs a dual-process approach: a coarse-to-fine process, followed by a fine-to-coarse process. The coarse-to-fine approach leverages the inherent knowledge of LLM to guide the understanding process from databases to tables and finally to columns. This approach provides a holistic understanding of the database structure and ensures contextual alignment. Conversely, the fine-to-coarse approach starts at the column level, offering a more accurate and nuanced understanding when stepping back to the table level. Experimental results on the Bird benchmark indicate that using descriptions…
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