A new dataset called VOLDOGER is shaking up how we train AI for visual-language tasks. Domain shift, where models struggle to adapt to different visual styles (like photos vs. cartoons), has been a persistent challenge. Traditionally, collecting diverse training data across various domains has been a costly and time-consuming hurdle, particularly for tasks involving both images and text descriptions. The creators of VOLDOGER have cleverly sidestepped this problem by using Large Language Models (LLMs) as automated annotators. They start with an existing dataset of real photos and their corresponding descriptions for tasks like image captioning, visual question answering, and visual entailment. Then, they employ an image generation model to transform these photos into different styles (cartoon, pencil drawing, oil painting), preserving the original semantic content. An LLM is used in every step of the process: First, it crafts prompts for image generation. Second, it checks the generated images for semantic consistency with the originals. Finally, the LLM generates style-appropriate captions, questions, and answers for the newly created images. This automation not only scales data generation but also allows for rapid adaptation to different visual styles, even those not initially anticipated. VOLDOGER has proven effective in training models that perform more consistently across various visual styles, reducing the effects of domain shift. But it's not without limitations. One key observation is that labels generated by the LLM can sometimes differ from human-generated labels, suggesting that fine-tuning these automated annotation methods remains an ongoing challenge. Despite this, VOLDOGER represents a significant step towards building more robust, adaptive vision-language models using the power of LLMs.