  Large Language Models (LLMs), originally shown to ace various text
comprehension tasks have also remarkably been shown to tackle table
comprehension tasks without specific training. While previous research has
explored LLM capabilities with tabular dataset tasks, our study assesses the
influence of $\textit{in-context learning}$,$ \textit{model scale}$,
$\textit{instruction tuning}$, and $\textit{domain biases}$ on Tabular Question
Answering (TQA). We evaluate the robustness of LLMs on Wikipedia-based
$\textbf{WTQ}$ and financial report-based $\textbf{TAT-QA}$ TQA datasets,
focusing on their ability to robustly interpret tabular data under various
augmentations and perturbations. Our findings indicate that instructions
significantly enhance performance, with recent models like Llama3 exhibiting
greater robustness over earlier versions. However, data contamination and
practical reliability issues persist, especially with WTQ. We highlight the
need for improved methodologies, including structure-aware self-attention
mechanisms and better handling of domain-specific tabular data, to develop more
reliable LLMs for table comprehension.
