The Interpretability Analysis of the Model Can Bring Improvements to the Text-to-SQL Task

Currently, AI technology is profoundly transforming the database landscape. Text - to - SQL, by innovating data provisioning to cater to the information retrieval and data analysis needs of a broader audience of everyday users, is emerging as a catalyst for propelling databases towards greater efficiency, collaboration, and intelligence. In recent years, text - to - SQL solutions leveraging large autoregressive models have continually surpassed existing methods on be nchmark datasets for multi - table complex queries (Zhu et al., 2024), such as Spider (Yu et al., 2018c) and BIRD (Li et al., 2023), attributed to their exceptional natural language underst anding and generation capabilities. In reality, it is highly prevalent for users of reporting systems to conduct simple queries, statistical analyses, and evaluations on consolidated single - report data derived from multi - table integration and field augmentation within databases. The single - table query dataset exemplified by WikiSQL (Zhong et al., 2017) aligns well with this application scenario. Despite its relatively straightforward synta x and lesser complexity when compared to datasets like Spider and BIRD (Deng et al., 2022), WikiSQL continues to serve as a pivotal benchmark for demonstrating the technical feasibility of converting natural language into simple SQL and validating the fundamental capabilities of models.