TripletQL

Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models

This paper proposes Semantic Triplet Restoration (STR), a table serialization protocol that rewrites each cell as an atomic fact <item path, feature path, value> to make header-cell alignments explicit for LLMs, replacing HTML/Markdown representations. The authors also introduce TripletQL, a query-aware router that selects relevant triplets per question. Evaluated on four Chinese and English table-QA benchmarks, STR matches or outperforms HTML-based baselines while reducing input token count. Benefits are most pronounced for smaller models and longer tables, suggesting value under constrained inference budgets.