Imagine an AI that can summarize medical reports, scientific papers, and government documents with equal fluency. That's the dream of domain adaptation, where a single model can seamlessly switch between specialized areas of knowledge. Researchers are actively exploring this frontier, and a new study delves into how well Large Language Models (LLMs) can truly adapt to the nuances of different domains for text summarization. The study, introducing a new evaluation suite called AdaptEval, examines the domain adaptation capabilities of 11 different LLMs, ranging from smaller, open-source models to giants like ChatGPT and GPT-4. AdaptEval doesn’t just look at traditional summarization metrics; it also measures how well the models incorporate domain-specific vocabulary and writing styles, offering deeper insight into how LLMs learn (or fail to learn) the language of a new field. The findings reveal a fascinating dynamic between model size and adaptation methods. In zero-shot learning, where models haven't seen any examples, bigger is generally better. However, with just a couple of examples (two-shot learning), smaller models can often match their larger counterparts, offering a more efficient path to adaptation. Interestingly, the conventional fine-tuning approach, where a model is retrained on a specific dataset, didn't improve domain vocabulary as much as expected, highlighting the challenges of truly mastering specialized language. Perhaps the most intriguing discovery from AdaptEval is how LLMs struggle with medical text. Even with the help of learning examples, medical summarization scores remained notably lower, hinting at the complexities of this domain and the need for more robust training methods. The journey toward fully adaptable AI summarization continues, and AdaptEval offers a promising benchmark to guide future research, pushing the boundaries of AI fluency across diverse domains.