AntiLeak-Bench: Preventing Data Contamination by Automatically
  Constructing Benchmarks with Updated Real-World Knowledge
Authors: name: Xiaobao Wu, name: Liangming Pan, name: Yuxi Xie, name: Ruiwen Zhou, name: Shuai Zhao, name: Yubo Ma, name: Mingzhe Du, name: Rui Mao, name: Anh Tuan Luu, name: William Yang Wang

Data contamination hinders fair LLM evaluation by introducing test data into
newer models' training sets. Existing studies solve this challenge by updating
benchmarks with newly collected data. However, they fail to guarantee
contamination-free evaluation as the newly collected data may contain
pre-existing knowledge, and their benchmark updates rely on intensive human
labor. To address these issues, we in this paper propose AntiLeak-Bench, an
automated anti-leakage benchmarking framework. Instead of simply using newly
collected data, we construct samples with explicitly new knowledge absent from
LLMs' training sets, which thus ensures strictly contamination-free evaluation.
We further design a fully automated workflow to build and update our benchmark
without human labor. This significantly reduces the cost of benchmark
maintenance to accommodate emerging LLMs. Through extensive experiments, we
highlight that data contamination likely exists before LLMs' cutoff time and
demonstrate AntiLeak-Bench effectively overcomes this challenge.