Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation

Understand

Generating SQLs from user queries is a long-standing challenge, where the accuracy of initial schema linking significantly impacts subsequent SQL generation performance.

• However, current schema linking models still struggle with missing relevant schema elements or an excess of redundant ones.
• A crucial reason for this is that commonly used metrics, recall and precision, fail to capture relevant element missing and thus cannot reflect actual schema linking performance.
• Motivated by this, we propose enhanced schema linking metrics by introducing a \textbf{restricted missing indicator}.