Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting

TL;DR

This paper introduces a training-free, structured prompting approach for table question-answering with large language models, improving accuracy and interpretability without fine-tuning.

Contribution

It proposes two novel prompting frameworks, TGN and PIP, for precise, interpretable, and resource-efficient table QA, achieving state-of-the-art results.

Findings

TGN improves over baseline by 3.8 points on TableBench.

PIP achieves SOTA on FeTaQa dataset.

Frameworks serve as supervision templates for fine-tuning small models.

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. 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 Inference Prompting (PIP), which enforces columns identification for explicit progressive row selection constraint according to the query. We evaluate 17 LLMs against 6 baselines on TableBench and FeTaQa dataset. On TableBench, TGN improves over the strongest baseline by 3.8 points, and on FeTaQa, PIP achieves SOTA performance over ReAct and Chain-of-Thought. Beyond inference-time gains, PIP and TGN can also serve as supervision templates to fine-tune small models, narrowing the performance gap to much larger architectures in resource-constrained settings, offering versatile and cost-efficient solution for TQA.