Large language models are increasingly trained on all the data ever produced
by humans. Many have raised concerns about the trustworthiness of public
benchmarks due to potential contamination in pre-training or fine-tuning
datasets. While most data decontamination efforts apply string matching (e.g.,
n-gram overlap) to remove benchmark data, we show that these methods are
insufficient, and simple variations of test data (e.g., paraphrasing,
translation) can easily bypass these decontamination measures. Furthermore, we
demonstrate that if such variation of test data is not eliminated, a 13B model
can easily overfit a test benchmark and achieve drastically high performance,
on par with GPT-4. We validate such observations in widely used benchmarks such
as MMLU, GSK8k, and HumanEval. To address this growing risk, we propose a
stronger LLM-based decontamination method and apply it to widely used
pre-training and fine-tuning datasets, revealing significant previously unknown
test overlap. For example, in pre-training sets such as RedPajama-Data-1T and
StarCoder-Data, we identified that 8-18\% of the HumanEval benchmark overlaps.
Interestingly, we also find such contamination in synthetic dataset generated
by GPT-3.5/4, suggesting a potential risk of unintentional contamination. We
urge the community to adopt stronger decontamination approaches when using
public benchmarks. Moreover, we call for the community to actively develop
fresh one-time exams to evaluate models accurately. Our decontamination tool is
publicly available at this https URL

大型语言模型的数据污染问题及其对应的检查与净化方法