  Large language models (LLMs) are increasingly applied for tabular tasks using
in-context learning. The prompt representation for a table may play a role in
the LLMs ability to process the table. Inspired by prior work, we generate a
collection of self-supervised structural tasks (e.g. navigate to a cell and
row; transpose the table) and evaluate the performance differences when using 8
formats. In contrast to past work, we introduce 8 noise operations inspired by
real-world messy data and adversarial inputs, and show that such operations can
impact LLM performance across formats for different structural understanding
tasks.
