Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models
This work addresses the need for better benchmarks to assess LMMs' alignment with diverse human needs in real-world scenarios, though it is incremental as it builds on existing evaluation frameworks.
The paper tackles the problem of evaluating large multimodal models (LMMs) for real-world personalization by proposing the Multi-Dimensional Insights (MDI) benchmark, which includes over 500 images and age-stratified questions, and finds that strong models like GPT-4o achieve 79% accuracy on age-related tasks, indicating room for improvement.
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/