Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models
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 grid modeling. However, these representations struggle to explicitly capture hierarchical structures and cross-dimensional dependencies, which can lead to misalignment between structural semantics and textual representations for non-standard tables. To address this issue, we propose an Orthogonal Hierarchical Decompositio
The rapid advancement of Large Language Models (LLMs) is pushing the boundaries of their application, revealing new challenges in handling complex, structured data formats like tables. This research emerges as a direct response to these limitations.
Improved table understanding is crucial for LLMs to effectively process and reason over a vast amount of enterprise and scientific data, which is frequently presented in complex table structures. This directly enhances the utility and reliability of AI systems for information extraction and decision support.
This new decomposition method fundamentally alters how LLMs interpret and interact with multi-level and merged-cell tables, moving beyond simplistic linearization to a more structure-aware approach. It allows for more accurate data extraction and semantic understanding from visually complex data.
- · AI developers
- · Data analytics platforms
- · Enterprise AI solutions
- · Academics researching LLMs
- · Legacy table processing software
LLMs will become significantly more adept at extracting insights and performing reasoning from complex tabular data.
This improved capability will unlock new enterprise applications for AI in finance, healthcare, and scientific research that heavily rely on structured data.
The enhanced understanding of tables could lead to more robust and less error-prone autonomous AI agents operating across various industries.
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