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English(EN) Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models

新的OHD框架增强了LLM的表格理解能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为正交分层分解（OHD）的新框架，以改进大语言模型（LLM）对复杂表格的理解。OHD使用正交树归纳法将不规则表格分解为列树和行树，捕捉层级依赖关系。这种保留结构的表示方式使LLM能够更好地解释多级表头、合并单元格和各种布局，在AITQA和HiTab等表格问答基准测试中表现优于现有方法。 AI

影响 这项研究可能有助于LLM从复杂的表格数据中提取和分析数据，从而提高准确性。

排序理由 该集群包含一篇详细介绍改进LLM能力新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的OHD框架增强了LLM的表格理解能力

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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Bin Cao, Huixian Lu, Chenwen Ma, Ting Wang, Ruizhe Li, Jing Fan ·

    面向大型语言模型的大规模表格理解的正交分层分解与结构感知

    arXiv:2602.01969v2 Announce Type: replace Abstract: Complex tables with multi-level headers, merged cells and heterogeneous layouts pose persistent challenges for LLMs in both understanding and reasoning. Existing approaches typically rely on table linearization or normalized gri…

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