We propose MM-Vet, an evaluation benchmark that examines large multimodal models (LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing abilities, such as solving math problems written on the blackboard, reasoning about events and celebrities in news images, and explaining visual jokes. Rapid model advancements pose challenges to evaluation benchmark development. Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking. To this end, we present MM-Vet, designed based on the insight that the intriguing ability to solve complicated tasks is often achieved by a generalist model being able to integrate different core vision-language (VL) capabilities. MM-Vet defines 6 core VL capabilities and examines the 16 integrations of interest derived from the capability combination. For evaluation metrics, we propose an LLM-based evaluator for open-ended outputs. The evaluator enables the evaluation across different question types and answer styles, resulting in a unified scoring metric. We evaluate representative LMMs on MM-Vet, providing insights into the capabilities of different LMM system paradigms and models. Code and data are available at https://github.com/yuweihao/MM-Vet.

翻译：我们提出MM-Vet，一个用于在复杂多模态任务上评估大型多模态模型（LMMs）的基准测试。近期LMMs展现出多种引人注目的能力，例如解答黑板上的数学题、推理新闻图片中的事件和名人、解释视觉笑话。模型的快速进步对基准测试开发提出了挑战。问题包括：（1）如何系统性地构建和评估复杂多模态任务；（2）如何设计适用于多种问答类型的评估指标；（3）如何在简单性能排名之外提供模型洞察。为此，我们提出MM-Vet，其设计基于以下见解：解决复杂任务的卓越能力通常源自通用模型整合不同核心视觉-语言（VL）能力。MM-Vet定义了6种核心VL能力，并考察了由这些能力组合衍生出的16种感兴趣整合。在评估指标方面，我们提出了一种基于LLM的评估器来处理开放型输出。该评估器支持跨不同问题类型和答案风格的评估，从而形成统一的评分指标。我们在MM-Vet上评估了代表性LMMs，深入分析了不同LMM系统范式与模型的能力。代码和数据开源于https://github.com/yuweihao/MM-Vet。