Large language models (LLMs) have shown incredible abilities, but how well do they truly grasp language when combined with visual information? A new research paper explores this by examining the linguistic capabilities of multimodal LLMs (MLLMs), which process both text and images. The researchers dove into how these models perform with limited examples, using techniques like in-context learning (ICL) and chain-of-thought (CoT) prompting. The study used the VALSE benchmark, a set of tasks designed to test how well models connect language concepts to what they "see" in images. This includes challenges like identifying whether objects exist, understanding plurals and counting, figuring out spatial relationships, recognizing actions, and resolving pronoun references (like figuring out who "he" refers to in a scene). The research team evaluated 14 different cutting-edge MLLMs, testing both zero-shot (no examples) and few-shot (a small number of examples) scenarios. The results were fascinating! They found that ICL and CoT prompting significantly boosted performance, especially in tasks requiring reasoning and understanding context. Giving the models similar examples, rather than random ones, also led to a noticeable improvement. Interestingly, models pre-trained on captioning datasets did well in zero-shot scenarios, but models pre-trained on a mix of images and text excelled when given a few examples. This suggests that while a broad understanding is helpful, targeted training combined with the ability to learn from limited examples is key for mastering complex tasks. While this study offers exciting insights, it also has limitations. The VALSE benchmark, while comprehensive, may not cover all real-world language use, and the study only looked at a subset of existing MLLMs. Future research could explore these limitations, investigating how these models perform in even more diverse and complex situations. This research is a step toward creating MLLMs that truly understand both what they see and what they read, paving the way for even more sophisticated AI systems.