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

Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs