FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs (2501.09957v2)

Abstract: To mitigate the hallucination and knowledge deficiency in LLMs, Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) has shown promising potential by utilizing KGs as external resource to enhance LLMs reasoning. However, existing KG-RAG approaches struggle with a trade-off between flexibility and retrieval quality. Modular methods prioritize flexibility by avoiding the use of KG-fine-tuned models during retrieval, leading to fixed retrieval strategies and suboptimal retrieval quality. Conversely, coupled methods embed KG information within models to improve retrieval quality, but at the expense of flexibility. In this paper, we propose a novel flexible modular KG-RAG framework, termed FRAG, which synergizes the advantages of both approaches. FRAG estimates the hop range of reasoning paths based solely on the query and classify it as either simple or complex. To match the complexity of the query, tailored pipelines are applied to ensure efficient and accurate reasoning path retrieval, thus fostering the final reasoning process. By using the query text instead of the KG to infer the structural information of reasoning paths and employing adaptable retrieval strategies, FRAG improves retrieval quality while maintaining flexibility. Moreover, FRAG does not require extra LLMs fine-tuning or calls, significantly boosting efficiency and conserving resources. Extensive experiments show that FRAG achieves state-of-the-art performance with high efficiency and low resource consumption.

• The paper introduces FRAG, a framework that synergizes modular and coupled KG-RAG approaches to enhance retrieval quality in language models.
• It employs a reasoning-aware module to classify queries and a three-phase retrieval pipeline using BFS for simple and Dijkstra's algorithm for complex queries.
• Experiments on WebQSP and CWQ demonstrate that FRAG achieves state-of-the-art performance with fewer LLM interactions and greater cost-efficiency.

An Overview of the FRAG Framework for Retrieval-Augmented Generation Using Knowledge Graphs

The paper introduces a novel framework named FRAG, which aims to improve the retrieval-augmented generation (RAG) capabilities of LLMs by leveraging knowledge graphs (KGs) as external resources. The primary goal of the FRAG framework is to mitigate the limitations of existing KG-based RAG methods that face a challenging trade-off between the flexibility of the modular approach and the retrieval quality of coupled approaches.

FRAG seeks to combine the advantages of both modular and coupled KG-RAG techniques. Modular methods, while flexible, often suffer from suboptimal retrieval quality due to dependency on fixed retrieval strategies without fine-tuning KGs information into models. Coupled methods, on the other hand, enhance retrieval quality by embedding KG data into models but at the expense of flexibility. FRAG synergizes these approaches by dynamically adjusting the retrieval process based on query complexity, bypassing the need for extensive model fine-tuning.

Methodology

The framework comprises two main components: the reasoning-aware module and the flexible-retrieval module. The reasoning-aware module classifies queries based on their complexity (simple or complex) by analyzing structural information inferred from the query itself. The classification is crucial as it helps in determining the appropriate retrieval strategy tailored to the query's complexity. By predicting the hop range of reasoning paths, the module aids in enhancing retrieval quality without relying on pre-existing KG data.

The flexible-retrieval module consists of a three-phase pipeline: preprocessing, retrieval, and postprocessing. During preprocessing, it narrows down the retrieval scope to subgraphs centered around significant entities. In the retrieval phase, for simple queries, reasoning paths are explored using a breadth-first search (BFS) which allows for comprehensive information acquisition. For complex queries, the framework employs Dijkstra's algorithm to efficiently pinpoint the shortest paths, thereby balancing information access and computational efficiency. The postprocessing phase filters and orders reasoning paths to ensure the most relevant and beneficial ones are utilized during LLM reasoning.

Experimental Results

Experiments conducted on benchmark datasets, namely WebQSP and CWQ, demonstrate that FRAG achieves state-of-the-art performance in modular KG-RAG tasks, surpassing previous methods like ToG across various LLM configurations. Particularly, FRAG maintains its superiority by requiring fewer LLM interactions and offering a cost-effective alternative to coupled KG-RAG methods like RoG, which require extensive fine-tuning. Notably, FRAG displays robustness across different hops, streamlining effective retrieval and reasoning for both simple and complex queries.

Implications and Future Directions

The FRAG framework sets a new benchmark for KG-RAG by providing an effective combination of flexibility and retrieval quality. It addresses the growing demand for scalable and adaptable RAG systems that can seamlessly integrate diverse external knowledge resources without extensive retraining or resource consumption. The insights derived from FRAG's approach toward dynamic retrieval adjustments suggest broader applicability in enhancing the reasoning accuracy of various AI models.

Looking ahead, further exploration into FRAG's adaptability to diverse KG structures and more complex reasoning scenarios can be anticipated. Additionally, investigating the potential integration of alternative retrieval and ranking algorithms might further enhance the balance between efficiency and performance, catering to different real-world AI applications. Through these avenues, FRAG draws attention to the evolving landscape of knowledge-centric AI research, inviting continued innovations at the intersection of retrieval strategies and LLM enhancement.