Beyond Linearization: Attributed Table Graphs for Table Reasoning
Quick summary
arXiv:2601.08444v2 Announce Type: replace Abstract: Table reasoning, a task to answer questions by reasoning over data presented in tables, is an important topic due to the prevalence of knowledge stored in tabular formats. Recent solutions use Large Language Models (LLMs) for their semantic understanding and reasoning capabilities. A common paradigm of such solutions linearizes tables to form plain texts that are served as input to LLMs. This paradigm has critical issues. It requires LLMs to infer row-column-cell relations from serialized inputs, makes evidence paths harder to trace, and is s
Key takeaways
- arXiv:2601.08444v2 Announce Type: replace Abstract: Table reasoning, a task to answer questions by reasoning over data presented in tables, is an important topic due to the prevalence of knowledge stored in tabular formats.
- Recent solutions use Large Language Models (LLMs) for their semantic understanding and reasoning capabilities.
- A common paradigm of such solutions linearizes tables to form plain texts that are served as input to LLMs.
Why it matters
“Beyond Linearization: Attributed Table Graphs for Table Reasoning” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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