Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task-specific perturbations, e.g., replacing key question entities or shuffling table columns. To systematically study the robustness of Table QA models, we propose a benchmark called RobuT, which builds upon existing Table QA datasets (WTQ, WikiSQL-Weak, and SQA) and includes human-annotated adversarial perturbations in terms of table header, table content, and question. Our results indicate that both state-of-the-art Table QA models and large language models (e.g., GPT-3) with few-shot learning falter in these adversarial sets. We propose to address this problem by using large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models. Our data and code is publicly available at https://github.com/yilunzhao/RobuT.

本研究通过构建RobuT数据集，含有表头、表内容及问题中的对抗性干扰，对现有表格问答模型的鲁棒性进行了评估，并提出利用大型语言模型生成对抗样本以增强训练，从而显著提高表格问答模型的鲁棒性。

RobuT: 对表格QA鲁棒性针对人类注释的对抗扰动的系统研究