SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Authors: name: Bohao Li, name: Rui Wang, name: Guangzhi Wang, name: Yuying Ge, name: Yixiao Ge, name: Ying Shan

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.