Current Limitations of Multimodal Retrieval-Augmented Generation (RAG)
Most existing benchmarks for RAG focus mainly on text for answering questions, which can be limiting. In many cases, it’s easier and more useful to retrieve visual information instead of text. This gap hinders the progress of large vision-language models (LVLMs) that need to effectively use various types of information.
Introducing MRAG-Bench
Researchers from UCLA and Stanford have developed MRAG-Bench, a benchmark that emphasizes visual information. This tool helps evaluate how well LVLMs perform in scenarios where visuals are more useful than text. MRAG-Bench includes:
- 16,130 images
- 1,353 human-annotated multiple-choice questions
- Nine distinct scenarios focused on visual knowledge advantages
Benchmark Structure
MRAG-Bench is organized into two main areas:
- Perspective Changes: Challenges models with different angles, visibility, and resolution.
- Transformative Changes: Focuses on how visual entities change over time or physically.
It includes a carefully curated set of 9,673 ground-truth images to ensure the benchmark reflects real-world visual understanding.
Evaluation Results
The results show that using visual information improves model performance significantly compared to text alone. For example:
- The best proprietary model, GPT-4o, improved by only 5.82% with visual augmentation.
- In contrast, human participants saw a 33.16% improvement, showcasing the gap in performance.
Proprietary models are also better at distinguishing high-quality visuals compared to open-source models, which often struggle.
Conclusion
MRAG-Bench is a groundbreaking evaluation tool for LVLMs, focusing on where visual information is more beneficial than text. This research highlights the significant gap between human and model performance in using visual data effectively.
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