Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, because Table Question-Answering (TQA) requires precise cell retrieval and multi-step structured reasoning. Existing work improves TQA either by fine-tuning or training LLMs on task-specific tabular data, but often lacks verifiable control over how the model navigates tables and derives answers. In this work, we propose a training-free TQA approach with two structured prompting frameworks: TableGrid Navigation (TGN), which iteratively navigates rows and columns via a three-module loop to locate evidence and refine answers, and Progressive Infer...
| Authors | Amritansh Maurya, Navjot Singh, Mohammed Javed et al. |
|---|---|
| Primary category | cs.IR |
| Published | 18 May 2026 |
| Last updated at source | 16 August 2026 |
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