EviProp: Seeded Relevance Diffusion on Chunk-Page Graphs for Long Multimodal Document Retrieval
Researchers have developed EviProp, a novel method for retrieving relevant pages from long, visually rich documents. Unlike existing approaches that score pages independently, EviProp models documents as multimodal Chunk-Page graphs. It uses seeded relevance diffusion, combining query-page similarity with chunk-level signals to improve retrieval accuracy. Experiments on benchmark datasets show EviProp outperforms traditional methods and leads to better downstream question-answering performance. AI
IMPACT Enhances retrieval accuracy for complex multimodal documents, potentially improving AI systems that rely on document understanding.