  The capabilities of large language models (LLMs) have been successfully
applied in the context of table representation learning. The recently proposed
tabular language models have reported state-of-the-art results across various
tasks for table interpretation. However, a closer look into the datasets
commonly used for evaluation reveals an entity leakage from the train set into
the test set. Motivated by this observation, we explore adversarial attacks
that represent a more realistic inference setup. Adversarial attacks on text
have been shown to greatly affect the performance of LLMs, but currently, there
are no attacks targeting tabular language models. In this paper, we propose an
evasive entity-swap attack for the column type annotation (CTA) task. Our CTA
attack is the first black-box attack on tables, where we employ a
similarity-based sampling strategy to generate adversarial examples. The
experimental results show that the proposed attack generates up to a 70% drop
in performance.

Computation and Language;Artificial Intelligence;Cryptography and Security