@inproceedings{ye-etal-2024-data,
title = "Data Contamination Calibration for Black-box {LLM}s",
author = "Ye, Wentao and
Hu, Jiaqi and
Li, Liyao and
Wang, Haobo and
Chen, Gang and
Zhao, Junbo",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.644",
pages = "10845--10861",
abstract = "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. PAC extends the popular MIA (Membership Inference Attack) {---} from machine learning community {---} by forming a more global target at detecting training data to Clarify invisible training data. As a pioneering work, PAC is very much plug-and-play that can be integrated with most (if not all) current white- and black-box LLMs. By extensive experiments, PAC outperforms existing methods by at least 4.5{\%}, towards data contamination detection on more 4 dataset formats, with more than 10 base LLMs. Besides, our application in real-world scenarios highlights the prominent presence of contamination and related issues.",
}
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%0 Conference Proceedings
%T Data Contamination Calibration for Black-box LLMs
%A Ye, Wentao
%A Hu, Jiaqi
%A Li, Liyao
%A Wang, Haobo
%A Chen, Gang
%A Zhao, Junbo
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F ye-etal-2024-data
%X 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. PAC extends the popular MIA (Membership Inference Attack) — from machine learning community — by forming a more global target at detecting training data to Clarify invisible training data. As a pioneering work, PAC is very much plug-and-play that can be integrated with most (if not all) current white- and black-box LLMs. By extensive experiments, PAC outperforms existing methods by at least 4.5%, towards data contamination detection on more 4 dataset formats, with more than 10 base LLMs. Besides, our application in real-world scenarios highlights the prominent presence of contamination and related issues.
%U https://aclanthology.org/2024.findings-acl.644
%P 10845-10861
Markdown (Informal)
[Data Contamination Calibration for Black-box LLMs](https://aclanthology.org/2024.findings-acl.644) (Ye et al., Findings 2024)
ACL
- Wentao Ye, Jiaqi Hu, Liyao Li, Haobo Wang, Gang Chen, and Junbo Zhao. 2024. Data Contamination Calibration for Black-box LLMs. In Findings of the Association for Computational Linguistics ACL 2024, pages 10845–10861, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.