Enhancing Robustness of Small-Scale Models for Text-to-SQL via Semantic Enhancement and Noise-Aware Methods

With the advancement of large language model technology, Text-to-SQL techniques have achieved significant progress and are widely applied in scenarios such as search engines. However, current small-scale models still face challenges, particularly poor robustness, especially when the database schema or user natural language queries lack clear semantic descriptions, resulting in decreased SQL generation accuracy. Although large-scale models demonstrate stronger robustness and higher accuracy, their high computational and deployment costs limit their use in private deployment settings. To address these issues, this paper proposes a reverse semantic enhancement method that leverages prior knowledge such as existing database values to enrich the semantic information of the database schema and user queries. Furthermore, to mitigate the noise introduced during semantic enhancement, a noise-aware training approach is designed to critique noisy data and identify effective semantic information, thereby improving the model’s resistance to interference. Extensive experiments on the Spider and BIRD datasets demonstrate the effectiveness of the proposed methods.