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，这是一个评估基准，用于检验大型多模态模型（LMMs）在复杂多模态任务上的表现。近期的大型多模态模型展现出了多种引人注目的能力，例如解算黑板上的数学题、推理新闻图片中的事件与名人、以及解读视觉笑话。模型的快速进步对评估基准的开发提出了挑战。问题包括：（1）如何系统性地构建并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）如何提供超越简单性能排名的模型洞察。为此，我们提出了MM-Vet，其设计基于以下洞察：解决复杂任务的出色能力通常源于通用模型能够整合不同的核心视觉-语言（VL）能力。MM-Vet定义了6项核心视觉-语言能力，并考察了由这些能力组合衍生的16种感兴趣的集成方式。在评估指标方面，我们提出了一种基于LLM的评估器，用于处理开放式的输出。该评估器支持跨不同问题类型和回答风格的评估，从而产生统一的评分指标。我们在MM-Vet上评估了具有代表性的LMMs，为不同LMM系统范式与模型的能力提供了深入见解。代码与数据可在https://github.com/yuweihao/MM-Vet获取。