Imagine training an AI to spot specific phrases like "recycling plastic" or "composting food scraps" within mountains of social media posts. Tricky, right? Especially when some phrases are rare. This is the challenge of multi-label text classification, where AI models struggle to identify less frequent categories. Researchers are now exploring ingenious ways to use large language models (LLMs) to help these AIs learn more effectively. A recent study delved into how different LLM prompting strategies could augment training data for classifying ecological texts. Essentially, they used LLMs to paraphrase existing examples, generate entirely new ones, or a mix of both, feeding this enriched data to their classifier. The results? A significant performance boost. The most effective strategy involved paraphrasing original text while explicitly telling the LLM which categories it belonged to. This method provided the classifier with a richer understanding of the connections between words and labels, improving its ability to spot those elusive phrases. While other strategies, like generating new examples from scratch, also improved performance, the paraphrasing method stood out. Interestingly, the research revealed that the type of LLM used also played a role. Specifically, the study tested Russian language models and datasets, showing that specialized models trained on predominantly Russian text performed best. This work offers valuable insights into leveraging the power of LLMs to overcome a major hurdle in text classification. By creating more diverse and representative training data, these techniques pave the way for more accurate and robust AI models. Future research will likely explore even more targeted data augmentation strategies, focusing on generating examples for the rarest categories. This will further enhance the AI's ability to learn from limited data and accurately classify even the most nuanced text.