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Recent advancements in multimodal techniques open exciting possibilities formodels excelling in diverse tasks involving text, audio, and image processing.Models like GPT-4V, blending computer vision and language modeling, excel incomplex text and image tasks. Numerous prior research endeavors have diligentlyexamined the performance of these Vision Large Language Models (VLLMs) acrosstasks like object detection, image captioning and others. However, theseanalyses often focus on evaluating the performance of each modality inisolation, lacking insights into their cross-modal interactions. Specifically,questions concerning whether these vision-language models execute vision andlanguage tasks consistently or independently have remained unanswered. In thisstudy, we draw inspiration from recent investigations into multilingualism andconduct a comprehensive analysis of model's cross-modal interactions. Weintroduce a systematic framework that quantifies the capability disparitiesbetween different modalities in the multi-modal setting and provide a set ofdatasets designed for these evaluations. Our findings reveal that models likeGPT-4V tend to perform consistently modalities when the tasks are relativelysimple. However, the trustworthiness of results derived from the visionmodality diminishes as the tasks become more challenging. Expanding on ourfindings, we introduce "Vision Description Prompting," a method thateffectively improves performance in challenging vision-related tasks.

Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond