Researchers propose a novel framework for improving interpretability of spreadsheets in LLM-driven RAG systems by splitting them into interpretable chunks using cell role annotation. This approach aims to address the 'spreadsheet-to-LLM bottleneck' by developing dimensionality-reduction techniques to flatten 2D unstructured spreadsheets into 1D text.