Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models
arXiv:2605.31550v1 Announce Type: new Abstract: Table question answering requires models to recover semantic relations encoded implicitly by two-dimensional layout, merged cells, and hierarchical headers. Current pipelines typically use HTML or Markdown as intermediate table representations, but these layout-oriented serializations introduce markup overhead and require large language models to infer header-cell alignments from row and column spans. We propose Semantic Triplet Restoration (STR), a protocol that rewrites each cell as an atomic fact , where the item path specifies the row-wise en
The proliferation of complex data tables and the increasing sophistication of Large Language Models (LLMs) necessitate more efficient and semantic methods for data extraction and understanding.
Improving how LLMs parse and understand tabular data directly impacts their utility in processing structured information, critical for analytical tasks across various industries.
Current inefficient table parsing methods are being replaced by a more semantic, triplet-based protocol that reduces overhead and enhances LLM accuracy in data interpretation.
- · Large Language Model developers
- · Data analysis platforms
- · Enterprises reliant on structured data
- · AI agents
- · Legacy table parsing solutions
- · Manual data entry roles
LLMs will become significantly more capable at extracting precise information from diverse and complex tables.
Enhanced table understanding could lead to more accurate AI agents and automation of data-intensive workflows.
This could accelerate the development of autonomous systems that can 'read' and interpret reports and documents as effectively as humans.
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