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Recent multimodal large language models (MLLMs) have shown promisinginstruction following capabilities on vision-language tasks. In this work, weintroduce VISUAL MODALITY INSTRUCTION (VIM), and investigate how wellmultimodal models can understand textual instructions provided in pixels,despite not being explicitly trained on such data during pretraining orfine-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 performancedisparity between the original TEM and VIM settings for open-source MLLMs,indicating that open-source MLLMs face greater challenges when text instructionis presented solely in image form. To address this issue, we train v-MLLM, ageneralizable model that is capable to conduct robust instruction following inboth text-modality and visual-modality instructions.

Text as Images: Can Multimodal Large Language Models Follow Printed Instructions in Pixels?