MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

This paper introduces MegaRAG, a novel approach to retrieval-augmented generation that leverages multimodal knowledge graphs to enhance the reasoning capabilities of large language models. The key innovation lies in incorporating visual cues into the knowledge graph construction, retrieval, and answer generation processes. This allows the model to perform cross-modal reasoning, leading to improved content understanding, especially for long-form, domain-specific content. The experimental results demonstrate that MegaRAG outperforms existing RAG-based approaches on both textual and multimodal corpora, suggesting a significant advancement in the field. The approach addresses the limitations of traditional RAG methods in handling complex, multimodal information.