SEED-Bench-2: Benchmarking Multimodal Large Language Models
Bohao Li Yuying Ge Yixiao Ge Guangzhi Wang Rui Wang Ruimao Zhang Ying Shan
Multimodal large language models (MLLMs), building upon the foundation of
powerful large language models (LLMs), have recently demonstrated exceptional
capabilities in generating not only texts but also images given interleaved
multimodal inputs (acting like a combination of GPT-4V and DALL-E 3). However,
existing MLLM benchmarks remain limited to assessing only models' comprehension
ability of single image-text inputs, failing to keep up with the strides made
in MLLMs. A comprehensive benchmark is imperative for investigating the
progress and uncovering the limitations of current MLLMs. In this work, we
categorize the capabilities of MLLMs into hierarchical levels from $L_0$ to
$L_4$ based on the modalities they can accept and generate, and propose
SEED-Bench-2, a comprehensive benchmark that evaluates the
\textbf{hierarchical} capabilities of MLLMs. Specifically, SEED-Bench-2
comprises 24K multiple-choice questions with accurate human annotations, which
spans 27 dimensions, including the evaluation of both text and image
generation. 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 23 prominent open-source
MLLMs and summarize valuable observations. By revealing the limitations of
existing MLLMs through extensive evaluations, we aim for SEED-Bench-2 to
provide insights that will motivate future research towards the goal of General
Artificial Intelligence. Dataset and evaluation code are available at
\href{https://github.com/AILab-CVC/SEED-Bench}