MMBenchTranslation site

In today’s AI field, evaluating the capabilities of multimodal large models has become a focal point for researchers and developers. MMBench, jointly developed by researchers from Shanghai AI Laboratory, Nanyang Technological University, The Chinese University of Hong Kong, National University of Singapore, and Zhejiang University, aims to provide a comprehensive evaluation system for multimodal models.

Website Introduction

MMBench focuses on assessing the capabilities of multimodal large models, targeting AI researchers, developers, and professionals interested in multimodal model performance.

Key Features

• Fine-grained Capability Evaluation: Covers 20 fine-grained capabilities, such as object detection, text recognition, action recognition, image understanding, and relational reasoning.
• Large-scale Question Bank: Comprises approximately 3,000 multiple-choice questions, ensuring comprehensive and in-depth evaluations.
• Innovative Assessment Methods: Employs circular shuffling of options to verify the consistency of output results, utilizing ChatGPT for precise matching of model responses to options, ensuring robustness and reproducibility of evaluation results.

Related Projects

MMBench, along with other multimodal evaluation tools like CMMLU, FlagEval, and HELM, collectively forms an ecosystem for multimodal model assessment.

Advantages

MMBench’s innovative assessment methods and comprehensive capability coverage provide significant advantages in the field of multimodal model evaluation, gaining widespread recognition in the industry.

Pricing

MMBench is currently open for free access, allowing users to directly visit its official website for evaluations.

Summary

MMBench, developed jointly by several renowned academic institutions, is dedicated to providing a comprehensive evaluation system for multimodal large models. Through its innovative features, users can gain in-depth understanding and assessment of multimodal model performance.