Preference Leakage: A Contamination Problem in LLM-as-a-judge
Large Language Models (LLMs) as judges and LLM-based data synthesis have
emerged as two fundamental LLM-driven data annotation methods in model
development. While their combination significantly enhances the efficiency of
model training and evaluation, little attention has been given to the potential
contamination brought by this new model development paradigm. In this work, we
expose preference leakage, a contamination problem in LLM-as-a-judge caused by
the relatedness between the synthetic data generators and LLM-based evaluators.
To study this issue, we first define three common relatednesses between data
generator LLM and judge LLM: being the same model, having an inheritance
relationship, and belonging to the same model family. Through extensive
experiments, we empirically confirm the bias of judges towards their related
student models caused by preference leakage across multiple LLM baselines and
benchmarks. Further analysis suggests that preference leakage is a pervasive
issue that is harder to detect compared to previously identified biases in
LLM-as-a-judge scenarios. All of these findings imply that preference leakage
is a widespread and challenging problem in the area of LLM-as-a-judge. We
release all codes and data at:
https://github.com/David-Li0406/Preference-Leakage.
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Dawei Li
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2025-02-18T09:34:59.780Z
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Dawei Li
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Renliang Sun
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2025-02-04T09:39:11.035Z
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Renliang Sun
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Yue Huang
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Ming Zhong
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2025-02-04T14:54:39.212Z
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Ming Zhong
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Bohan Jiang
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2025-08-07T10:40:48.854Z
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Bohan Jiang
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Bohan-Jiang
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Jiawei Han
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Xiangliang Zhang
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2025-02-04T17:14:47.621Z
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zhangxiangliang
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ZhangXiangliang
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Wei Wang
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2025-02-04T17:14:54.615Z
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Wei Wang
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WeiWang
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Huan Liu
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Large Language Models (LLMs) as judges and LLM-based data synthesis have
emerged as two fundamental LLM-driven data annotation methods in model
development. While their combination significantly enhances the efficiency of
model training and evaluation, little attention has been given to the potential
contamination brought by this new model development paradigm. In this work, we
expose preference leakage, a contamination problem in LLM-as-a-judge caused by
the relatedness between the synthetic data generators and LLM-based evaluators.
To study this issue, we first define three common relatednesses between data
generator LLM and judge LLM: being the same model, having an inheritance
relationship, and belonging to the same model family. Through extensive
experiments, we empirically confirm the bias of judges towards their related
student models caused by preference leakage across multiple LLM baselines and
benchmarks. Further analysis suggests that preference leakage is a pervasive
issue that is harder to detect compared to previously identified biases in
LLM-as-a-judge scenarios. All of these findings imply that preference leakage
is a widespread and challenging problem in the area of LLM-as-a-judge. We
release all codes and data at:
https://github.com/David-Li0406/Preference-Leakage.
Preference leakage, a bias in LLM-as-a-judge systems caused by the relatedness between synthetic data generators and evaluators, has been empirically confirmed and found to be pervasive and challenging.
LLMs LLM-as-a-judge LLM-based data synthesis data annotation preference leakage relatedness bias benchmarks