Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment of generative models, by introducing a benchmark named SEED-Bench. SEED-Bench consists of 19K multiple choice questions with accurate human annotations (x 6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality. We develop an advanced pipeline for generating multiple-choice questions that target specific evaluation dimensions, integrating both automatic filtering and manual verification processes. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 18 models across all 12 dimensions, covering both the spatial and temporal understanding. By revealing the limitations of existing MLLMs through evaluation results, we aim for SEED-Bench to provide insights for motivating future research. We will launch and consistently maintain a leaderboard to provide a platform for the community to assess and investigate model capability.

翻译：基于强大的大语言模型（LLM），近年来生成的生成式多模态大语言模型（MLLM）作为关键研究领域备受关注，展现出卓越的理解与生成能力。本文以评估MLLM的生成式理解为起点，引入名为SEED-Bench的基准测试，旨在为全面评估生成式模型奠定基础。SEED-Bench包含19K道选择题，均经过精准人工标注（规模比现有基准测试大6倍），涵盖图像和视频模态理解的12个评估维度。我们开发了针对特定评估维度生成选择题的先进流程，整合了自动筛选与人工验证过程。基于人工标注的真实选项的选择题，能够客观高效地评估模型性能，无需在评估过程中引入人工或GPT干预。我们进一步评估了18个模型在全部12个维度上的表现，覆盖空间与时间理解。通过评估结果揭示现有MLLM的局限性，我们希望SEED-Bench能为未来研究提供启示。我们将持续发布并维护排行榜，为社区评估和探索模型能力提供平台。