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

RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations