Interpreting spreadsheet grids improves question answering with large language models
Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure
Artificial IntelligenceSoftware Engineering
Summary
Spreadsheets have complex layouts that make it hard for computers to understand them just as tables. The authors show that labeling the roles of spreadsheet cells helps computers chunk the data more meaningfully, improving how big language models can generate answers from spreadsheets. However, this method cannot fully grasp all the complex relationships in the spreadsheet. They suggest future work should focus on turning the two-dimensional structure into text in a way that language models can better handle.
What this means in practice
- •For business analysts: Improve the accuracy of question answering systems that extract insights from complex company spreadsheets by using interpretable chunking.
- •For spreadsheet software developers: Enhance spreadsheet tools by integrating semantic cell role detection to aid automated summarization and data interpretation features.
Authors
Zofia Smoleń
Abstract
Semantic cell annotation improves chunking interpretability for spreadsheets in LLM-driven RAG systems, aiding answer generation through enriched context rather than improved retrieval accuracy. We propose a novel framework of splitting any spreadsheet into interpretable chunks using cell role annotation. Our framework beats the state of the art, yet it faces a hard ceiling. Spreadsheets are fundamentally two-dimensional unstructured data with continuous relationships and infinite potential cell roles. Because classification models are restricted to finite, pre-defined classes, they cannot perfectly capture this structural nuance, even with human-level annotation. We show that addressing the spreadsheet-to-LLM bottleneck requires moving beyond discrete cell classification. Instead, the field must develop dimensionality-reduction techniques to directly flatten 2D unstructured spreadsheets into 1D unstructured text. Text chunks would be easier for downstream RAG to interpret and generate from.