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This paper introduces VizRAG, a novel retrieval-augmented generation (RAG) system that integrates hypergraph visualization to enhance knowledge retrieval and reconstruction. By moving beyond traditional binary relationships and incorporating complex n-ary atomic facts, VizRAG leverages the visual capabilities of multimodal large language models (MLLMs) to improve performance. Experimental results show that VizRAG significantly outperforms existing unimodal RAG frameworks, highlighting the effectiveness of hypergraph awareness in enhancing retrieval tasks.
Hypergraph visualization in RAG systems boosts performance by effectively utilizing the visual perception capabilities of multimodal large language models.
Hypergraph-based RAG systems surpass traditional graph-based approaches by organizing complex n-ary atomic facts among entities, rather than relying solely on binary relationships. Despite the advancements in multimodal large language models (MLLMs) with enhanced visual capabilities, current hypergraph-based RAG frameworks predominantly restrict knowledge retrieval and reconstruction to a unimodal, text-centric paradigm. This limitation prevents them from fully leveraging the powerful visual perception capabilities of modern MLLMs. To address this gap, we systematically explore the integration of hypergraph awareness in RAG systems through visual cues. By incorporating visual representations of hypergraphs into the RAG pipeline, we introduce VizRAG, the first RAG system to support visual hypergraph structure awareness. Experimental results demonstrate that VizRAG significantly outperforms strong baselines, validating the promising potential of hypergraph visualization as a novel approach for RAG systems.
