Elephants Never Forget: Memorization and Learning of Tabular Data in
  Large Language Models
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. Specifically, we introduce a variety of different techniques to
assess whether a language model has seen a tabular dataset during training.
This investigation reveals that LLMs have memorized many popular tabular
datasets verbatim. We then compare the few-shot learning performance of LLMs on
datasets that were seen during training to the performance on datasets released
after training. We find that LLMs perform better on datasets seen during
training, indicating that memorization leads to overfitting. At the same time,
LLMs show non-trivial performance on novel datasets and are surprisingly robust
to data transformations. We then investigate the in-context statistical
learning abilities of LLMs. Without fine-tuning, we find them to be limited.
This suggests that much of the few-shot performance on novel datasets is due to
the LLM's world knowledge. Overall, our results highlight the importance of
testing whether an LLM has seen an evaluation dataset during pre-training. We
make the exposure tests we developed available as the tabmemcheck Python
package at https://github.com/interpretml/LLM-Tabular-Memorization-Checker
6615f5e024c94fd8a222195e
Sebastian Bordt
6615f5e024c94fd8a222195f
Harsha Nori
6615f5e024c94fd8a2221960
Vanessa Rodrigues
6615f5e024c94fd8a2221961
Besmira Nushi
claimed_verified
2025-05-27T07:59:30.650Z
638ee2ed848625f9fad11034
https://cdn-avatars.huggingface.co/v1/production/uploads/1670309301345-638ee2ed848625f9fad11034.jpeg
Besmira Nushi
user
nushib
6615f5e024c94fd8a2221962
Rich Caruana
Elephants Never Forget: Memorization and Learning of Tabular Data in
  Large Language Models
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. Specifically, we introduce a variety of different techniques to
assess whether a language model has seen a tabular dataset during training.
This investigation reveals that LLMs have memorized many popular tabular
datasets verbatim. We then compare the few-shot learning performance of LLMs on
datasets that were seen during training to the performance on datasets released
after training. We find that LLMs perform better on datasets seen during
training, indicating that memorization leads to overfitting. At the same time,
LLMs show non-trivial performance on novel datasets and are surprisingly robust
to data transformations. We then investigate the in-context statistical
learning abilities of LLMs. Without fine-tuning, we find them to be limited.
This suggests that much of the few-shot performance on novel datasets is due to
the LLM's world knowledge. Overall, our results highlight the importance of
testing whether an LLM has seen an evaluation dataset during pre-training. We
make the exposure tests we developed available as the tabmemcheck Python
package at https://github.com/interpretml/LLM-Tabular-Memorization-Checker
LLMs have memorized popular tabular datasets and perform better on seen datasets, indicating overfitting, while still showing robust performance on novel datasets through world knowledge.
Large Language Models LLMs few-shot learning data contamination memorization in-context statistical learning