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We introduce VISUAL EMBEDDED INSTRUCTION (VIM), a new framework designed toevaluate the visual instruction following capability of Multimodal LargeLanguage Models (MLLMs). As illustrated in Figure 2, VIM challenges the MLLMsby embedding the instructions into the visual scenes, demanding strong visualinterpretative skills for instruction following. We adapt VIM to variousbenchmarks, including VQAv2, MME, MM-Vet, and RefCOCO series, compose a VIMbench, and probe diverse MLLMs across three distinct in-context learningsettings: Zero Shot, One Shot, and Pair Shot. We observe that there is asignificant performance disparity between the open-source MLLMs and GPT-4V,implying that their proficiency in visual instruction comprehension is not upto par. Our results highlight a promising direction for the enhancement ofMLLMs capabilities on instruction following. We aim VIM to serve as a usefulnorm for advancing the state of the art and driving further progress in thefield.

VIM: Probing Multimodal Large Language Models for Visual Embedded Instruction Following