  Multimodal Large Language Models (MLLMs) have exhibited impressive
capabilities in visual understanding and reasoning, providing sightly
reasonable answers, such as image descriptions. This has spurred extensive
research on the evaluation of MLLMs. Most evaluation benchmarks assume that
incorrect answers indicate a lack of understanding of the visual content.
However, our findings reveal that, in many cases, MLLMs answer questions
incorrectly despite correctly understanding the visual content. This suggests
that incorrect answers do not necessarily imply a lack of comprehension but may
instead result from lacking robustness to leading questions. To comprehensively
measure MLLMs' understanding capability and robustness to leading questions, we
introduce a MultiModal Robustness benchmark (MMR). MMR contains paired positive
and negative questions across 12 categories, meticulously annotated by humans.
We evaluate 18 leading MLLMs on the MMB benchmark, revealing that MLLMs suffer
from fragility to leading questions despite understanding the visual content.
To enhance MLLMs' understanding capability and robustness, we further present a
training set with paired positive and negative visual question-answer samples.
Experiments verify that MLLMs' robustness can be significantly enhanced by
tuning on this new training set. The benchmark, training set, and code can be
found at https://github.com/BAAI-DCAI/Multimodal-Robustness-Benchmark.
