[2502.15723] Balancing Content Size in RAG-Text2SQL System

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a summary:LLMS models appeared as a promising solution to convert natural language queries into SQL orders, allowing the interaction of a smooth database. However, the text systems to SQL (Text2SQL) face inherent restrictions, hallucinations, outdated knowledge, and irreparable logic. To address these challenges, the integration of the generation of retrieval (RAG) has gained with TExt2SQL models. Rag works as a retrieval mechanism, and provides basic contextual information, such as table schemes and descriptive data, to enhance the process of generating the query. Despite its capabilities, Rag + Text2SQL systems are subject to the quality and size of recovered documents. While the wealthiest document content can improve the importance of the scheme and the accuracy of the retrieval, it also offers noise, which increases the risk of hallucinations and reduces loyalty to the query while increasing the rapid size of the Text2SQL model. This research examines the exact comparison between the size of the document and quality, with the aim of achieving a balance that improves the performance of the system. The main thresholds are determined when the performance deterioration occurs, as well as implementable strategies to mitigate these challenges. In addition, we explore the phenomenon of hallucinations in Text2SQL models, focusing on the decisive role to display the coordinated document in reducing errors. The results of us provide a road map to enhance the durability of the Rag + Text2SQL systems, providing practical visions of applications in the real world.