Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs· 多粒度上下文增强的多模态知识图谱RAG
Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages structured knowledge to provide (M)LLMs with high-quality external information. Building on these works, recent studies have explored multimodal knowledge graphs (MMKGs) as knowledge bases for GraphRAG. This enables Graph RAG to integrate knowledge across multiple modalities, thereby further enhancing its performance. However, existing MMKG-based RAG methods generally follow a common pipeline in which different modalities are largely processed independently before being fusion. As a result, textual context is only used to a limited extent during visual information extraction
提出多粒度上下文增强的多模态知识图谱RAG，提升模型性能。
- 核心方法
- 通过多粒度上下文增强策略，整合多模态知识图谱中的知识，改进RAG模型在多模态信息处理中的文本上下文利用。
- 适合谁读
- 研究者和工程师
- 要解决的问题
- 现有基于多模态知识图谱的RAG方法处理不同模态数据时独立性高，导致文本上下文在视觉信息提取中利用有限。
- 关键实验
- 实验结果表明，该方法在多个多模态任务上相比基线模型有显著提升，但具体实验数据未详细列出。
- 主要贡献
- 提出了一种新的多粒度上下文增强方法，有效提高了RAG模型在多模态知识图谱中的综合性能。
- 意义与局限
- 该方法将有助于进一步减少LLMs和MLLMs的幻觉问题，提升其在多模态任务中的可靠性和准确性。局限在于模型复杂度增加，可能需要更多计算资源。