SOURCE-LINKED INTELLIGENCE
Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure
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 per
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-17T17:22:43.000Z
- arXiv · Artificial Intelligence · 2026-09-17T17:22:43.000Z
First collected: 2026-09-19T20:26:32.566Z. This is not the publication date.