Elephants Never Forget: Testing Language Models for Memorization of Tabular Data

arXiv:2403.06644v1 Announce Type: new
Abstract: While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over. In this work, we address this concern for tabular data. Starting with simple qualitative tests for whether an LLM knows the names and values of features, we introduce a variety of different techniques to assess the degrees of contamination, including statistical tests for conditional distribution …

arxiv cs.cl cs.lg data elephants language language models tabular tabular data testing type