Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering
Table Question Answering (TableQA) aims to reason over tables to answer user queries. Existing research treats all questions uniformly and evaluates solely through overall accuracy, obscuring a critical reality that LLMs excel at simple lookups yet struggle with complex operations like aggregation and arithmetic. To reveal this disparity, we introduce a novel \emph{Operation-wise TableQA} task with a fine-grained question taxonomy and release two datasets named WikiTQ-ow and TabFact-ow for evaluation. As for modeling bottlenecks, existing methods flatten tables into linearized texts, disrupting inherent structures and inducing the ``lost-in-the-middle'' issue, which poses a primary barrie...
| Authors | Guixin Su, Qiankun Pi, Mayi Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 June 2026 |
| Last updated at source | 14 September 2026 |
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