Imagine asking an AI a simple question about a table of data, like "Who won the most gold medals?" Seems easy, right? But for AI, tables can be surprisingly tricky. It's not the data itself, but how it's presented. Current AI models mostly rely on fixed table formats. This can be a bottleneck, especially with the rise of powerful Large Language Models (LLMs). Think of it like trying to read a recipe written in a language you don't understand. The information is there, but you can't access it. Researchers from Harbin Institute of Technology and iFLYTEK Research have tackled this problem head-on with a novel approach called FLEXTAF. This innovative method focuses on giving LLMs the flexibility to "read" tables in the most effective way possible. FLEXTAF works in two ways. First, FLEXTAF-Single trains a classifier to predict the best table format for a given question and LLM. It's like having a translator choose the right language for the recipe. Second, FLEXTAF-Vote combines answers from multiple table formats and picks the most consistent one, a bit like asking several translators and choosing the most common translation. This method leverages the strengths of different formats. Testing FLEXTAF on challenging datasets like WikiTableQuestions and TabFact, the researchers found significant performance boosts. FLEXTAF-Single and FLEXTAF-Vote outperformed fixed format approaches by an average of 2.3% and 4.8%, respectively. These results show the importance of flexibility in how AI processes information. One key finding is that different AI models, even those with similar architecture, prefer different table formats for the same question. This highlights the need for personalized approaches to AI learning and reasoning. This research opens up new avenues for making AI smarter and more efficient at handling the vast amount of tabular data in our world. From scientific research and financial analysis to everyday tasks like comparing product specifications, flexible table formats can unlock the full potential of LLMs. While FLEXTAF shows promising results, challenges remain, particularly in improving the accuracy of format prediction and the sophistication of the voting mechanism. Further research could explore more nuanced formats and develop better classifiers to match the specific needs of different LLMs and tasks. The future of AI depends on its ability to understand and reason with the data we give it. FLEXTAF is a significant step toward making AI more flexible, robust, and adaptable to the diverse world of tabular data.