HVM-GraphRAG: A New Framework for Multimodal QA on Complex Documents
A new framework called HVM-GraphRAG has been developed by researchers to enhance question answering capabilities for intricate documents. This holistic-view multimodal GraphRAG framework tackles two major challenges faced by current multimodal GraphRAG techniques: unreliable indexing of cross-modal evidence and the high costs associated with graph traversal. By utilizing a holistic perspective for graph construction, HVM-GraphRAG minimizes conflicting updates and noise, establishing dependable indices between concept-level graph nodes and corresponding multimodal chunks. During the retrieval process, it navigates a streamlined concept-level graph, allowing direct access to supporting evidence via the constructed index, thus circumventing the need for expensive traversal through dense entity-level graphs. This study is available on arXiv under ID 2607.24861.
Key facts
- HVM-GraphRAG is a holistic-view multimodal GraphRAG framework.
- It addresses unreliable cross-modal evidence indexing and expensive graph traversal.
- Uses a holistic view to guide graph construction.
- Reduces noisy and conflicting graph updates.
- Builds reliable indices between concept-level graph nodes and multimodal chunks.
- Searches over compact concept-level graph during retrieval.
- Directly accesses supporting evidence through constructed index.
- Published on arXiv with ID 2607.24861.
Entities
Institutions
- arXiv