Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis (2506.04142v1)

Abstract: The development of 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

• The paper's main contribution is introducing a method to detect and suppress shortcut neurons that bias LLM evaluation due to data contamination.
• It employs comparative and causal analysis techniques, including activation patching, to align contaminated model scores with authentic performance.
• Experimental results on LLaMA and Mistral demonstrate reduced overestimation, setting a path for more trustworthy and intrinsic AI evaluations.

Trustworthy Evaluation of LLMs via Shortcut Neuron Analysis

This paper introduces a novel methodology aimed at establishing robust evaluation for LLMs by addressing data contamination through shortcut neuron analysis. Currently, many LLM evaluations are hindered by data contamination issues, where models are inadvertently trained on evaluation data, leading to non-representative benchmark scores. This not only undermines fairness but inflates the perceived capabilities of models, posing significant challenges in assessing the true performance of LLMs.

Key Contributions

The paper presents a method that shifts focus from the traditional creation of dynamic benchmarks to directly tackling contamination within model parameters. Instead of frequently developing new benchmarks — a process that is resource-intensive — the authors propose an in-depth analysis of neurons in the contaminated models. The core innovation lies in identifying what the authors term "shortcut neurons," specific neurons that have acquired shortcut solutions during the training process, leading to the overestimation of a model's capabilities.

To achieve a trustworthy evaluation, the paper introduces an approach called the Shortcut Neuron Patching method. This approach leverages a combination of comparative analysis and causal inference:

1. Comparative Analysis: By contrasting neuron activations between contaminated models and their uncontaminated counterparts, the authors pinpoint neurons linked to memory shortcuts.
2. Causal Analysis: Activation patching is utilized to assess causal effects, aiming to restore the contaminated model's scores to reflect genuine capabilities without impacting normal model functions.

The research demonstrates how these shortcut neurons can be suppressed to circumvent contamination biases — thereby restoring trustworthy evaluation. The practical experiments carried out on LLaMA and Mistral architectures validate this methodology, showing significant declines in accuracy for contaminated models under the new evaluation framework, aligning their results closer to proven uncontaminated benchmarks.

Implications and Future Work

The methodology has substantial implications for both theoretical explorations and practical implementations in AI research. By focusing on shortcut neurons, the paper proposes that understanding the inner mechanisms of LLMs is crucial for achieving fairness in model evaluations. Practically, this approach can improve the reliability of LLM assessments, aiding developers and researchers in forming accurate understandings of model capabilities.

Going forward, applying this method across different architectures and varied datasets might help generalize the findings, potentially serving as a foundation for transparent evaluations in AI. Given the dynamic nature of AI evolution, consistently suppressing shortcut-induced biases is pivotal for genuine model appraisals, enhancing trust in AI systems.

Ultimately, this work underscores the critical need for evolving beyond benchmark-driven evaluations, advocating for deeper, model-intrinsic analyses that lead to authentically discerning AI capabilities.