title: Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis
summary: 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
paper_authors._id: 684132cb725b7fb67f68ffb8
paper_authors.name: Kejian Zhu
paper_authors._id: 684132cb725b7fb67f68ffb9
paper_authors.name: Shangqing Tu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:56:11.501Z
paper_authors._id: 648c48d8c0ddeee6df5b6d22
paper_authors.avatarUrl: /avatars/8706b0b16dfc332b96c91d3ced31bd0b.svg
paper_authors.fullname: Shangqing Tu
paper_authors.type: user
paper_authors.user: tsq2000
paper_authors._id: 684132cb725b7fb67f68ffba
paper_authors.name: Zhuoran Jin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:56:17.500Z
paper_authors._id: 643379416c6ecd58798421b3
paper_authors.avatarUrl: /avatars/831db7eab2663abc33b176cf386b02f2.svg
paper_authors.fullname: Zhuoran Jin
paper_authors.type: user
paper_authors.user: jinzhuoran
paper_authors._id: 684132cb725b7fb67f68ffbb
paper_authors.name: Lei Hou
paper_authors._id: 684132cb725b7fb67f68ffbc
paper_authors.name: Juanzi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T12:56:23.839Z
paper_authors._id: 65df8cbc2705d9672f55d1aa
paper_authors.avatarUrl: /avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
paper_authors.fullname: Juanzi Li
paper_authors.type: user
paper_authors.user: juanli
paper_authors._id: 684132cb725b7fb67f68ffbd
paper_authors.name: Jun Zhao
paper_title: Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis
paper_summary: 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
paper_ai_summary: A method called shortcut neuron patching identifies and suppresses shortcut neurons in language models to mitigate data contamination issues in trustworthy evaluations.
paper_ai_keywords: 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