@inproceedings{wu-etal-2025-antileakbench,
    title = "{A}nti{L}eak{B}ench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge",
    author = "Wu, Xiaobao  and
      Pan, Liangming  and
      Xie, Yuxi  and
      Zhou, Ruiwen  and
      Zhao, Shuai  and
      Ma, Yubo  and
      Du, Mingzhe  and
      Mao, Rui  and
      Luu, Anh Tuan  and
      Wang, William Yang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.901/",
    pages = "18403--18419",
    ISBN = "979-8-89176-251-0",
    abstract = "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 that AntiLeak-Bench effectively overcomes this challenge."
}