Evading Data Contamination Detection for Language Models is (too) Easy

Understand

Large language models are widespread, with their performance on benchmarks frequently guiding user preferences for one model over another.

• However, the vast amount of data these models are trained on can inadvertently lead to contamination with public benchmarks, thus compromising performance measurements.
• While recently developed contamination detection methods try to address this issue, they overlook the possibility of deliberate contamination by malicious model providers aiming to evade detection.
• We argue that this setting is of crucial importance as it casts doubt on the reliability of public benchmarks.