Microsoft researchers have developed a groundbreaking framework called SPREADSHEETLLM that enhances the processing and analysis of complex spreadsheet data. This innovative approach improves performance on spreadsheet understanding tasks while significantly reducing computational costs. Key features of SPREADSHEETLLM include SHEETCOMPRESSOR, which compresses spreadsheets by up to 96%, allowing for handling larger datasets within token limits. Structural anchor extraction identifies key rows and columns, preserving critical layout information. Inverted-index translation efficiently encodes cell contents and addresses to minimize redundancy, and data format-aware aggregation groups cells with similar formats. SPREADSHEETLLM outperformed previous methods by 12.3% in experiments, achieving state-of-the-art results in spreadsheet table detection and question-answering tasks. The framework tested well with LLMs like GPT-4, demonstrating promise in fine-tuned versions. Additionally, SPREADSHEETLLM reduced processing costs by 96% compared to standard methods, showing significant advancements in spreadsheet analysis.