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New Method Promises More Honest LLM Evaluation by Targeting 'Shortcut' Neurons

A new research paper is proposing a novel way to evaluate large language models (LLMs) that aims to circumvent a growing problem: data contamination, where models are trained on benchmark datasets they are later evaluated on, leading to artificially inflated scores. The approach, detailed in a paper titled “Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis,” identifies and “patches” specific neurons within the LLM that contribute to these “shortcut” solutions, offering a more accurate picture of a model’s true capabilities.

The researchers start from the observation that current LLM evaluations are often based on public benchmarks that are vulnerable to data contamination. This can result in an overestimation of model performance, particularly for those that have “memorized” the benchmark dataset. To tackle this, the team proposes analyzing the internal mechanisms of potentially contaminated models, positing that models develop “shortcut solutions” to benchmark problems. These shortcuts allow the models to achieve high scores without genuinely understanding the underlying tasks.

The core of the method involves identifying “shortcut neurons” – specific parameters within the model that contribute to these superficial solutions. This identification process involves two key steps: a “comparative analysis” comparing the activation patterns of neurons in contaminated versus uncontaminated models, and a “causal analysis” that uses “activation patching” to determine the causal impact of individual neurons on the model’s performance. If a particular neuron is identified as a shortcut neuron it restores the true score of the contaminated model, and (b) does not affect the normal ability of the model.

For example, consider a LLM trained on a mathematical problem dataset like GSM8K. A shortcut neuron might learn to recognize a specific phrase or pattern in the problem statement and directly associate it with the correct answer, bypassing the need for genuine mathematical reasoning.

Once identified, these shortcut neurons are “patched” by replacing their activations with those from a clean, uncontaminated model. This effectively disables the shortcut pathways, forcing the model to rely on more robust and generalizable problem-solving strategies. The model’s performance is then re-evaluated.

The researchers validated their method on LLaMA and Mistral architectures, demonstrating that patching shortcut neurons significantly reduces the accuracy of contaminated models on benchmark tasks, while having minimal impact on their performance on other tasks designed to measure general reasoning abilities. This suggests that the method effectively targets shortcuts without compromising the model’s overall intelligence.

The team also shows that their evaluation metric is strongly correlated with MixEval, a state-of-the-art evaluation benchmark known for its trustworthiness. This correlation, which reached a Spearman coefficient exceeding 0.95, further strengthens the claim that the proposed method offers a more realistic assessment of LLM capabilities. The researchers also highlight the generalizability of their method across different benchmarks and hyperparameter settings.

The work offers a promising avenue for developing more trustworthy and informative LLM evaluations, moving beyond simple benchmark scores to a more nuanced understanding of a model’s internal workings and true capabilities. The open-sourced code accompanying the paper facilitates further research and adoption of this approach.