Imagine an AI that could rewrite any text in countless ways, from a toddler's simple words to a scholar's complex prose. This isn't science fiction—researchers are exploring how to use large language models (LLMs) to “rephrase” existing text data, potentially unlocking vast amounts of new training material for even more powerful AI. This process involves breaking down text into smaller chunks, feeding them to an LLM with specific instructions (like “rewrite this for a toddler” or “make this sound like Wikipedia”), and then piecing the rephrased text back together. Why bother rewriting the entire web? As LLMs grow larger, they need an ever-increasing diet of data to learn from. Rephrasing offers a way to create synthetic data, expanding the training pool while potentially smoothing out biases in the original text. This research tested rephrasing on various datasets, including C4, CulturaX (a multilingual dataset), and FineWeb-Edu (a high-quality dataset). Interestingly, the results showed that rephrasing worked best when combined with the original text, especially for lower-quality or multilingual datasets. For instance, German, Spanish, and Italian text saw significant improvements after being rephrased and mixed back with the original. However, for already high-quality English text, the gains were less pronounced. The study also explored how the size of the LLM used for rephrasing affects the quality of the rewritten text. Surprisingly, bigger wasn’t always better. A medium-sized model often outperformed its larger counterpart, highlighting the importance of careful model selection in this process. Finally, the research looked at the downstream effects of training LLMs on this rephrased data. While results varied depending on the benchmark used, they suggest that rephrasing can indeed lead to improvements in downstream tasks, especially when fine-tuning these models for specific applications. This work opens exciting possibilities. Rephrasing could be crucial in addressing the data hunger of ever-growing LLMs, allowing us to create customized datasets for training AI in specialized fields, generating multilingual data with ease, and potentially mitigating biases present in original sources. However, the complex interplay between model size, data quality, and downstream performance warrants further investigation as we navigate the future of AI and its insatiable appetite for information.