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This research addresses the spreadsheet-to-LLM bottleneck by proposing dimensionality-reduction techniques to flatten two-dimensional unstructured data into one-dimensional text, which is more interpretable for downstream retrieval-augmented generation systems. While semantic cell annotation improves context enrichment, the authors argue that discrete classification models face a fundamental ceiling in capturing the continuous relationships inherent in spreadsheet structures. The study highlights a necessary shift away from finite class-based approaches toward methods that directly handle the infinite potential roles of cells within LLM-driven workflows.
Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure
arXiv:2609.20732v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
arXiv:2609.20732 [cs.AI]
(or arXiv:2609.20732v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.20732 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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