不需训练，用两套提示框架让大模型精准读表答题。
Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
- 通过三模块循环迭代定位表格行列，精准找证据
- 在TableBench上比最强基线高3.8分，在FeTaQa上达最新纪录
- 适合资源有限场景，可作小模型监督模板
大语言模型在自然语言任务中表现优异，但在表格数据上的表现仍需关注，因为表格问答（TQA）需要精确的单元格检索和多步结构化推理。现有方法通过微调或在特定表格数据上训练模型提升性能，但往往无法验证模型如何导航表格并推导答案。本文提出一种无需训练的TQA方法，包含两个结构化提示框架：表网格导航（TGN），通过三模块循环迭代遍历行与列以定位证据并优化答案；渐进式推理提示（PIP），通过显式约束列识别来实现按查询逐步选择行。我们在TableBench和FeTaQa数据集上评估17个LLM与6个基线。在TableBench上，TGN比最强基线提升3.8分；在FeTaQa上，PIP超越ReAct和思维链方法达到新纪录。此外，PIP与TGN还可作为监督模板用于微调小型模型，在资源受限环境下缩小与大型模型的性能差距，提供灵活且低成本的TQA解决方案。
原文摘要 · Abstract (English)
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.
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