Data Contamination Calibration for Black-box LLMs

Understand

The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size.

• However, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination, i.e.
• the benchmark data is used for training.
• In this work, we propose a holistic method named Polarized Augment Calibration (PAC) along with a new to-be-released dataset to detect the contaminated data and diminish the contamination effect.