  Recent multimodal large language models (MLLMs) have shown promising
instruction following capabilities on vision-language tasks. In this work, we
introduce VISUAL MODALITY INSTRUCTION (VIM), and investigate how well
multimodal models can understand textual instructions provided in pixels,
despite not being explicitly trained on such data during pretraining or
fine-tuning. We adapt VIM to eight benchmarks, including OKVQA, MM-Vet,
MathVista, MMMU, and probe diverse MLLMs in both the text-modality instruction
(TEM) setting and VIM setting. Notably, we observe a significant performance
disparity between the original TEM and VIM settings for open-source MLLMs,
indicating that open-source MLLMs face greater challenges when text instruction
is presented solely in image form. To address this issue, we train v-MLLM, a
generalizable model that is capable to conduct robust instruction following in
both text-modality and visual-modality instructions.
