Large language models (LLMs) have made impressive strides in various tasks, but math word problems, especially those involving tables, remain a challenge. Why? One key factor is the lack of high-quality training data. Creating these datasets is time-consuming and expensive, which limits the progress of LLMs in this crucial area. A new research paper introduces a clever solution: a 'template-driven, LLM-paraphrased' framework called TeLL. This innovative approach uses templates based on real math problems to generate new problems while ensuring their accuracy. Then, it leverages the language skills of another LLM to paraphrase these template-based problems, making them more diverse and realistic. Think of it like this: the first LLM creates the mathematical backbone, and the second LLM adds the story and context. Researchers used TeLL to create a new dataset called TabMWP-TeLL, focusing on enriching solutions with clear, step-by-step reasoning. They found that these detailed explanations are crucial for LLMs to truly understand the problem-solving process. The results? LLMs trained on TabMWP-TeLL showed significant improvements, especially on complex problems like those involving stem-and-leaf plots, which traditionally trip up LLMs. This research shows that while bigger models are important, feeding them the *right* kind of data is just as crucial, especially when it comes to mathematical reasoning. By combining templated accuracy with LLM-powered diversity, we can help LLMs become much better math students, potentially unlocking a wide range of applications in fields requiring complex calculations and analysis. The future of LLMs doing your taxes might not be so far off after all!