Balancing Content Size in RAG-Text2SQL System

TL;DR

This paper investigates how document size and quality affect retrieval-augmented generation in Text2SQL systems, identifying optimal thresholds and strategies to reduce hallucinations and improve robustness.

Contribution

It provides a detailed analysis of the trade-offs between document size and quality in RAG-Text2SQL systems, offering strategies to optimize performance and minimize hallucinations.

Findings

Optimal document size thresholds identified for best performance.

Curated document presentation reduces hallucinations.

Strategies proposed to balance content richness and noise.

Abstract

Large Language Models (LLMs) have emerged as a promising solution for converting natural language queries into SQL commands, enabling seamless database interaction. However, these Text-to-SQL (Text2SQL) systems face inherent limitations, hallucinations, outdated knowledge, and untraceable reasoning. To address these challenges, the integration of retrieval-augmented generation (RAG) with Text2SQL models has gained traction. RAG serves as a retrieval mechanism, providing essential contextual information, such as table schemas and metadata, to enhance the query generation process. Despite their potential, RAG + Text2SQL systems are susceptible to the quality and size of retrieved documents. While richer document content can improve schema relevance and retrieval accuracy, it also introduces noise, increasing the risk of hallucinations and reducing query fidelity as the prompt size of the Text2SQL model increases. This research investigates the nuanced trade-off between document size and quality, aiming to strike a balance that optimizes system performance. Key thresholds are identified where performance degradation occurs, along with actionable strategies to mitigate these challenges. Additionally, we explore the phenomenon of hallucinations in Text2SQL models, emphasizing the critical role of curated document presentation in minimizing errors. Our findings provide a roadmap for enhancing the robustness of RAG + Text2SQL systems, offering practical insights for real-world applications.