Tables are everywhere, packed with valuable data. But for all their power, large language models (LLMs) have traditionally struggled to understand the complex relationships within large tables. Imagine trying to make sense of a massive spreadsheet with hundreds of columns and thousands of rows – it's not easy. LLMs face a similar challenge. Existing methods work well for smaller tables but fall short when dealing with the intricate, interconnected data found in real-world scenarios. This is where "Tree-of-Table" comes in. This new approach empowers LLMs to grasp the complexities of large tables by first condensing the data based on the specific question being asked. Think of it like focusing a lens – the model zooms in on the most relevant information. Then, the method cleverly breaks down the table into a hierarchical, tree-like structure. This makes it easier for the LLM to navigate the data and unravel the connections between different pieces of information. Finally, the LLM systematically walks through this "Table-Tree" to derive the answers, piece by piece. This step-by-step approach mirrors how humans solve problems, making the process more efficient and accurate. Tests on various datasets, including the massive BIRD dataset, show that Tree-of-Table outperforms previous methods, especially with large, complex tables. This breakthrough has significant implications for various fields. From finance to healthcare, the ability to extract insights from complex tables can revolutionize decision-making and lead to more data-driven strategies. However, there are still limitations. Balancing the depth and breadth of the "Table-Tree" requires careful tuning. Future research will focus on making this process more adaptable. Ultimately, Tree-of-Table represents a significant step towards unlocking the full potential of LLMs in understanding the world of data, opening up new possibilities for how we interact with and learn from information.