用图结构建模文档布局，让RAG更好理解图文混合信息。
SuperRAG: Beyond RAG with Layout-Aware Graph Modeling
- 通过解析文档布局构建图文表连接的图结构
- 在4个基准测试中显著提升多模态问答性能
- 适合需要跨模态推理的复杂文档理解任务
本文提出一种面向多模态RAG的布局感知图建模方法。与传统仅处理扁平文本片段的RAG不同，该方法基于文档布局解析构建图结构，保留文本块、表格和图表之间的语义关联。这种表示方式使系统能够有效应对需跨模态信息融合的复杂问题。为验证其有效性，构建了采用强健组件的灵活RAG流水线。在四个基准测试集上的实验结果表明，布局感知建模显著提升了RAG系统的性能表现。
原文摘要 · Abstract (English)
This paper introduces layout-aware graph modeling for multimodal RAG. Different from traditional RAG methods that mostly deal with flat text chunks, the proposed method takes into account the relationship of multimodalities by using a graph structure. To do that, a graph modeling structure is defined based on document layout parsing. The structure of an input document is retained with the connection of text chunks, tables, and figures. This representation allows the method to handle complex questions that require information from multimodalities. To confirm the efficiency of the graph modeling, a flexible RAG pipeline is developed using robust components. Experimental results on four benchmark test sets confirm the contribution of the layout-aware modeling for performance improvement of the RAG pipeline.
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