Summary of Mrag-bench: Vision-centric Evaluation For Retrieval-augmented Multimodal Models, by Wenbo Hu et al.
MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
by Wenbo Hu, Jia-Chen Gu, Zi-Yi Dou, Mohsen Fayyaz, Pan Lu, Kai-Wei Chang, Nanyun Peng
First submitted to arxiv on: 10 Oct 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 MRAG-Bench, a multimodal retrieval-augmented generation benchmark that evaluates vision-language models (LVLMs) on their ability to utilize visually augmented knowledge for question answering. The benchmark consists of 16,130 images and 1,353 human-annotated multiple-choice questions across 9 distinct scenarios, including varying viewpoints. The authors conduct an evaluation of 10 open-source and 4 proprietary LVLMs, showing that all models exhibit greater improvements when augmented with images compared to textual knowledge. The top-performing model, GPT-4o, faces challenges in effectively leveraging retrieved knowledge, achieving only a 5.82% improvement with ground-truth information. This highlights the importance of MRAG-Bench in encouraging the community to enhance LVLMs’ ability to utilize retrieved visual knowledge more effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper creates a new way to test how well computers can understand and use images to answer questions. Right now, most tests focus on using text to answer questions. But sometimes it’s better or easier to use images instead. The team created a big dataset of 16,000 images with 1,300 questions about what the images show. They tested different computer models on this task and found that all of them got better results when they used images to help answer the questions. This shows how important it is for computers to be able to use images effectively. |
Keywords
» Artificial intelligence » Gpt » Question answering » Retrieval augmented generation
