arXiv:2605.20254v1 Announce Type: cross 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
The proliferation of Large Language Models (LLMs) has revealed a critical need for efficient and verifiable methods to handle structured data, which current LLM architectures struggle with.
Improving LLM performance on tabular data enhances the accuracy and reliability of AI systems for business intelligence, data analysis, and decision-making.
This research introduces a training-free approach to Table Question-Answering (TQA) that provides more control and clarity over LLM reasoning processes on structured data.
- · AI developers
- · Data scientists
- · Enterprises with large datasets
- · NLP researchers
- · Companies reliant on labor-intensive data analysis
- · Less efficient TQA methods
LLMs can now more reliably extract and reason with information embedded in tables.
Automation of data reporting and analytical tasks becomes more feasible across industries.
Enhanced trust in AI-driven insights could lead to broader adoption of LLMs for critical strategic decisions.
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Read at arXiv cs.LG