Tabular Representation, Noisy Operators, and Impacts on Table Structure
  Understanding Tasks in LLMs
Ananya Singha José Cambronero Sumit Gulwani Vu Le Chris Parnin
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.