@inproceedings{
singha2023tabular,
title={Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in {LLM}s},
author={Ananya Singha and Jos{\'e} Cambronero and Sumit Gulwani and Vu Le and Chris Parnin},
booktitle={NeurIPS 2023 Second Table Representation Learning Workshop},
year={2023},
url={https://openreview.net/forum?id=Ld5UCpiT07}
}
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.
Tabular Representation, Noisy Operators, and Impacts on Table Structure Understanding Tasks in LLMs
Ananya Singha José Cambronero Sumit Gulwani Vu Le Chris Parnin