Summary of Multi-dimensional Insights: Benchmarking Real-world Personalization in Large Multimodal Models, by Yifan Zhang et al.
Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models
by YiFan Zhang, Shanglin Lei, Runqi Qiao, Zhuoma GongQue, Xiaoshuai Song, Guanting Dong, Qiuna Tan, Zhe Wei, Peiqing Yang, Ye Tian, Yadong Xue, Xiaofei Wang, Honggang Zhang
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 The proposed Multi-Dimensional Insights (MDI) benchmark aims to comprehensively evaluate large multimodal models’ capabilities in real-world scenarios. The MDI-Benchmark includes over 500 images covering six common life scenarios, accompanied by simple and complex questions that assess the model’s understanding and ability to analyze and reason beyond basic content. Notably, the benchmark stratifies questions into three age categories: young people, middle-aged people, and older people, allowing for a detailed assessment of models’ capabilities in meeting different age groups’ needs and preferences. The strong GPT-4o model achieves 79% accuracy on age-related tasks, indicating room for improvement. The MDI-Benchmark opens new pathways for aligning real-world personalization in LMMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to test how well AI models understand the world. It’s called the Multi-Dimensional Insights (MDI) benchmark. This benchmark includes many images that show different scenarios from everyday life, like shopping or cooking. The images come with simple and harder questions that help us see if the model can really understand what it’s seeing. The best part is that the questions are divided into three groups based on age: young people, middle-aged people, and older people. This helps us see how well AI models can work for people of different ages. |
Keywords
» Artificial intelligence » Gpt
