@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://aclanthology.org/2025.acl-long.901/",
doi = "10.18653/v1/2025.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."
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%0 Conference Proceedings
%T AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge
%A Wu, Xiaobao
%A Pan, Liangming
%A Xie, Yuxi
%A Zhou, Ruiwen
%A Zhao, Shuai
%A Ma, Yubo
%A Du, Mingzhe
%A Mao, Rui
%A Luu, Anh Tuan
%A Wang, William Yang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wu-etal-2025-antileakbench
%X 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.
%R 10.18653/v1/2025.acl-long.901
%U https://aclanthology.org/2025.acl-long.901/
%U https://doi.org/10.18653/v1/2025.acl-long.901
%P 18403-18419
Markdown (Informal)
[AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge](https://aclanthology.org/2025.acl-long.901/) (Wu et al., ACL 2025)
ACL
- Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, and William Yang Wang. 2025. AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18403–18419, Vienna, Austria. Association for Computational Linguistics.