arXiv Artificial Intelligence

Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure

Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure

Quick summary

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

Key takeaways

  • 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.

Why it matters

“Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗