Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
The paper presents a novel approach to Table Question-Answering (TQA) using two frameworks: TableGrid Navigation (TGN) and Progressive Inference Prompting (PIP). These methods aim to enhance the performance of Large Language Models (LLMs) on tabular data without the need for extensive training. The results indicate significant improvements in TQA tasks, making it a promising solution for resource-constrained environments.
- ▪The proposed TGN framework navigates tables iteratively to refine answers.
- ▪PIP enforces column identification for better row selection based on queries.
- ▪The evaluation shows TGN improves performance by 3.8 points on the TableBench dataset.
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Computer Science > Information Retrieval arXiv:2605.20254 (cs) [Submitted on 18 May 2026] Title:Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting Authors:Amritansh Maurya, Navjot Singh, Mohammed Javed, Omar Moured View a PDF of the paper titled Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting, by Amritansh Maurya and 2 other authors View PDF HTML (experimental) Abstract: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.
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