Data contamination, i.e., the presence of test data from downstream tasks in
the training data of large language models (LLMs), is a potential major issue
in understanding LLMs' effectiveness on other tasks. We propose a
straightforward yet effective method for identifying data contamination within
LLMs. At its core, our approach starts by identifying potential contamination
in individual instances that are drawn from a small random sample; using this
information, our approach then assesses if an entire dataset partition is
contaminated. To estimate contamination of individual instances, we employ
"guided instruction:" a prompt consisting of the dataset name, partition type,
and the initial segment of a reference instance, asking the LLM to complete it.
An instance is flagged as contaminated if the LLM's output either exactly or
closely matches the latter segment of the reference. To understand if an entire
partition is contaminated, we propose two ideas. The first idea marks a dataset
partition as contaminated if the average overlap score with the reference
instances (as measured by ROUGE or BLEURT) is statistically significantly
better with the guided instruction vs. a general instruction that does not
include the dataset and partition name. The second idea marks a dataset as
contaminated if a classifier based on GPT-4 with in-context learning prompting
marks multiple instances as contaminated. Our best method achieves an accuracy
between 92% and 100% in detecting if an LLM is contaminated with seven
datasets, containing train and test/validation partitions, when contrasted with
manual evaluation by human expert. Further, our findings indicate that GPT-4 is
contaminated with AG News, WNLI, and XSum datasets.

在理解大型语言模型（LLM）对其他任务的有效性中，数据污染（即，在训练数据中存在来自下游任务的测试数据）可能是一个重要问题。我们提出了一种简单但有效的方法来识别 LLMs 中的数据污染，该方法通过识别来自小型随机样本的个别实例中的潜在污染，然后评估整个数据集分区是否受到了污染。