Sparse Attention-Driven Retrieval-Augmented Generation for Financial Insights

Abstract

The integration of deep learning with external knowledge retrieval has significantly advanced the capabilities of natural language understanding and generation models. Retrieval-Augmented Generation (RAG) frameworks have particularly demonstrated promising performance in information-dense tasks, where grounding on external documents is essential. However, the dense attention mechanisms typically employed in these frameworks result in high computational costs and suboptimal scalability when applied to large financial datasets. To address these challenges, we propose a Sparse Attention-Driven Retrieval-Augmented Generation (SA-RAG) model that optimizes both retrieval and generation by employing a scarified attention mechanism. Our approach leverages sparse transformer architecture to enhance focus on the most relevant retrieved documents while reducing the overhead associated with full attention. We evaluate the performance of SA-RAG on a range of financial NLP tasks, including financial report summarization, market sentiment analysis, and earnings call question answering. The results show a substantial improvement in generation accuracy, relevance, and efficiency over conventional RAG models. This paper presents the architectural design, experimental setup, results, and implications of employing sparse attention in RAG models for financial insights.