Title: KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG
Primary area: generative models
Keywords: Retrieval Augmented Generation, Knowledge Graph, Direct Preference Optimization

Abstract: Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to access external knowledge sources, significantly enhancing their ability to perform knowledge-intensive tasks. As RAG systems become increasingly vital for real-world applications, improving their ablility of leveraginge externel knowledge has emerged as a critical research direction. Recent studies have explored fine-tuning approaches to enhance LLMs' adaptability in RAG scenarios. However, the inherent complexity of RAG systems makes it challenging to achieve robust performance without substantial amounts of high-quality training data, particularly when handling multi-hop reasoning and other complex tasks that require rich information content and intricate relationships between knowledge elements.

In this paper, we present KARE-RAG (Knowledge-Aware Refinement and Enhancement for RAG), which introduces a novel strategy for RAG optimization. Our key insight is that by training models to construct structured knowledge representations as intermediate outputs, models can learn robust information discrimination strategies from minimal training data while maintaining the flexibility of end-to-end generation. Experiments show our method achieves superior OOD performance while requiring substantially less training data. Notably, these improvements are achieved without compromising general capabilities or requiring modifications to standard RAG inference pipelines. Our findings establish that targeted training strategies focusing on knowledge organization can unlock more efficient optimization pathways for RAG systems. All data and code will be publicly available on Github.