Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models
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| Authors | Yibin Zhao et al. |
| Year | 2026 |
| Field | NLP |
| arXiv | 2605.31550 |
| Download | |
| Categories | cs.CL |
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 <item path, feature path, value>, where the item path specifies the row-wise entity, the feature path specifies the hierarchical attribute, and the value contains the cell content. We also present TripletQL, a lightweight query-aware router that uses STR to select an appropriate rendering or filtered subset of triplets for each question. Across four Chinese and English table-QA benchmarks, STR matches or improves upon HTML-based baselines while reducing input tokens. The relative benefit grows for smaller language models and longer table contexts, suggesting that explicit semantic representations are especially useful under constrained inference budgets. Code and data are available at https://github.com/Phoenix-ni/STR.git .
Engineering Breakdown
The Problem
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.
The Approach
We propose Semantic Triplet Restoration (STR), a protocol that rewrites each cell as an atomic fact <item path, feature path, value>, where the item path specifies the row-wise entity, the feature path specifies the hierarchical attribute, and the value contains the cell content.
Key Results
Code and data are available at https://github.com/Phoenix-ni/STR.git .
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Large language models
- Transformers
- Text generation
- Natural language processing
- Language understanding
- Restoration
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