Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis
The development of large language models (LLMs) depends on trustworthy
evaluation. However, most current evaluations rely on public benchmarks, which
are prone to data contamination issues that significantly compromise fairness.
Previous researches have focused on constructing dynamic benchmarks to address
contamination. However, continuously building new benchmarks is costly and
cyclical. In this work, we aim to tackle contamination by analyzing the
mechanisms of contaminated models themselves. Through our experiments, we
discover that the overestimation of contaminated models is likely due to
parameters acquiring shortcut solutions in training. We further propose a novel
method for identifying shortcut neurons through comparative and causal
analysis. Building on this, we introduce an evaluation method called shortcut
neuron patching to suppress shortcut neurons. Experiments validate the
effectiveness of our approach in mitigating contamination. Additionally, our
evaluation results exhibit a strong linear correlation with MixEval, a recently
released trustworthy benchmark, achieving a Spearman coefficient (rho)
exceeding 0.95. This high correlation indicates that our method closely reveals
true capabilities of the models and is trustworthy. We conduct further
experiments to demonstrate the generalizability of our method across various
benchmarks and hyperparameter settings. Code:
https://github.com/GaryStack/Trustworthy-Evaluation
684132cb725b7fb67f68ffb8
Kejian Zhu
claimed_verified
2025-06-11T09:23:32.911Z
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zkj
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JokerJan
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Shangqing Tu
admin_assigned
2025-06-05T12:56:11.501Z
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Shangqing Tu
user
tsq2000
684132cb725b7fb67f68ffba
Zhuoran Jin
admin_assigned
2025-06-05T12:56:17.500Z
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Zhuoran Jin
user
jinzhuoran
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Lei Hou
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Juanzi Li
admin_assigned
2025-06-05T12:56:23.839Z
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Juanzi Li
user
juanli
684132cb725b7fb67f68ffbd
Jun Zhao
Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis
The development of large language models (LLMs) depends on trustworthy
evaluation. However, most current evaluations rely on public benchmarks, which
are prone to data contamination issues that significantly compromise fairness.
Previous researches have focused on constructing dynamic benchmarks to address
contamination. However, continuously building new benchmarks is costly and
cyclical. In this work, we aim to tackle contamination by analyzing the
mechanisms of contaminated models themselves. Through our experiments, we
discover that the overestimation of contaminated models is likely due to
parameters acquiring shortcut solutions in training. We further propose a novel
method for identifying shortcut neurons through comparative and causal
analysis. Building on this, we introduce an evaluation method called shortcut
neuron patching to suppress shortcut neurons. Experiments validate the
effectiveness of our approach in mitigating contamination. Additionally, our
evaluation results exhibit a strong linear correlation with MixEval, a recently
released trustworthy benchmark, achieving a Spearman coefficient (rho)
exceeding 0.95. This high correlation indicates that our method closely reveals
true capabilities of the models and is trustworthy. We conduct further
experiments to demonstrate the generalizability of our method across various
benchmarks and hyperparameter settings. Code:
https://github.com/GaryStack/Trustworthy-Evaluation
A method called shortcut neuron patching identifies and suppresses shortcut neurons in language models to mitigate data contamination issues in trustworthy evaluations.
large language models LLMs trustworthy evaluation data contamination benchmarks dynamic benchmarks shortcut solutions shortcut neurons comparative analysis causal analysis shortcut neuron patching MixEval Spearman coefficient