Summary of M3docrag: Multi-modal Retrieval Is What You Need For Multi-page Multi-document Understanding, by Jaemin Cho et al.
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding
by Jaemin Cho, Debanjan Mahata, Ozan Irsoy, Yujie He, Mohit Bansal
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel multi-modal framework called M3DocRAG for visual question answering (VQA) from documents. Existing methods focus on single-page documents or text-based retrieval-augmented generation, but struggle with real-world scenarios where questions span multiple pages or require information from visual elements like figures. M3DocRAG addresses these limitations by accommodating various document contexts, question hops, and evidence modalities using a multi-modal retriever and a multi-modal language model (MLM). The framework is evaluated on three benchmarks: M3DocVQA, MMLongBench-Doc, and MP-DocVQA. Results show that M3DocRAG with ColPali and Qwen2-VL 7B achieves superior performance compared to many strong baselines, including state-of-the-art results in MP-DocVQA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for computers to answer questions from documents. Right now, there are problems with the current methods because they can’t handle big documents or ignore important information like pictures and charts. The researchers created a new system called M3DocRAG that can handle these challenges. It uses special computer programs to find relevant documents and answer questions by combining text and visual information. The team tested their system on many different types of documents and it performed very well, especially in situations where the answers come from multiple pages or images. |
Keywords
» Artificial intelligence » Language model » Multi modal » Question answering » Retrieval augmented generation
