MuirBench: A Comprehensive Benchmark for Robust Multi-image
  Understanding
Authors: name: Fei Wang, name: Xingyu Fu, name: James Y. Huang, name: Zekun Li, name: Qin Liu, name: Xiaogeng Liu, name: Mingyu Derek Ma, name: Nan Xu, name: Wenxuan Zhou, name: Kai Zhang, name: Tianyi Lorena Yan, name: Wenjie Jacky Mo, name: Hsiang-Hui Liu, name: Pan Lu, name: Chunyuan Li, name: Chaowei Xiao, name: Kai-Wei Chang, name: Dan Roth, name: Sheng Zhang, name: Hoifung Poon, name: Muhao Chen

We introduce MuirBench, a comprehensive benchmark that focuses on robust
multi-image understanding capabilities of multimodal LLMs. MuirBench consists
of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that
involve 10 categories of multi-image relations (e.g., multiview, temporal
relations). Comprising 11,264 images and 2,600 multiple-choice questions,
MuirBench is created in a pairwise manner, where each standard instance is
paired with an unanswerable variant that has minimal semantic differences, in
order for a reliable assessment. Evaluated upon 20 recent multi-modal LLMs, our
results reveal that even the best-performing models like GPT-4o and Gemini Pro
find it challenging to solve MuirBench, achieving 68.0% and 49.3% in accuracy.
Open-source multimodal LLMs trained on single images can hardly generalize to
multi-image questions, hovering below 33.3% in accuracy. These results
highlight the importance of MuirBench in encouraging the community to develop
multimodal LLMs that can look beyond a single image, suggesting potential
pathways for future improvements.