新框架提升表格结构解析能力
Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure
研究团队提出新框架，能更精准解析表格结构，对LLM处理表格数据很有帮助。
研究提出一种框架，通过语义单元格标注将任何表格分割成可解释的块，用于LLM驱动的RAG系统。该框架通过丰富上下文而非提升检索精度来辅助回答生成。它比现有方法更优，但面临硬性瓶颈。表格是二维无结构数据，具有连续关系和无限潜在单元格角色。分类模型受限于有限预定义类别，无法完美捕捉这种结构细微之处，即使使用人类级标注。解决表格到LLM瓶颈需要超越离散单元格分类，开发降维技术直接将二维无结构表格展平为1D无结构文本。文本块对下游RAG的解析和生成更友好。
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 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.
