Multi-Dimensional Insights: Benchmarking Real-World Personalization in
  Large Multimodal Models
Authors: name: YiFan Zhang, name: Shanglin Lei, name: Runqi Qiao, name: Zhuoma GongQue, name: Xiaoshuai Song, name: Guanting Dong, name: Qiuna Tan, name: Zhe Wei, name: Peiqing Yang, name: Ye Tian, name: Yadong Xue, name: Xiaofei Wang, name: Honggang Zhang

The rapidly developing field of large multimodal models (LMMs) has led to the
emergence of diverse models with remarkable capabilities. However, existing
benchmarks fail to comprehensively, objectively and accurately evaluate whether
LMMs align with the diverse needs of humans in real-world scenarios. To bridge
this gap, we propose the Multi-Dimensional Insights (MDI) benchmark, which
includes over 500 images covering six common scenarios of human life. Notably,
the MDI-Benchmark offers two significant advantages over existing evaluations:
(1) Each image is accompanied by two types of questions: simple questions to
assess the model's understanding of the image, and complex questions to
evaluate the model's ability to analyze and reason beyond basic content. (2)
Recognizing that people of different age groups have varying needs and
perspectives when faced with the same scenario, our benchmark stratifies
questions into three age categories: young people, middle-aged people, and
older people. This design allows for a detailed assessment of LMMs'
capabilities in meeting the preferences and needs of different age groups. With
MDI-Benchmark, the strong model like GPT-4o achieve 79% accuracy on age-related
tasks, indicating that existing LMMs still have considerable room for
improvement in addressing real-world applications. Looking ahead, we anticipate
that the MDI-Benchmark will open new pathways for aligning real-world
personalization in LMMs. The MDI-Benchmark data and evaluation code are
available at https://mdi-benchmark.github.io/