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Mechanistic Interpretability as Statistical Estimation: A Variance Analysis of EAP-IG

Created by
  • Haebom

Author

Maxime M eloux, Fran\c{c}ois Portet, Maxime Peyrard

Outline

Developing reliable AI requires understanding the internal computation of models. Mechanistic Interpretability (MI) aims to uncover the algorithmic mechanisms of model behavior. This paper argues that interpretability methods, such as circuit discovery, suffer from variance and robustness issues due to their reliance on statistical estimation. Through a systematic stability analysis of EAP-IG, a state-of-the-art circuit discovery methodology, we evaluate various controlled perturbations, including input resampling, prompt reconfiguration, hyperparameter variation, and noise injection within causal analysis. Across various models and tasks, EAP-IG exhibits high structural variance and hyperparameter sensitivity, raising questions about the robustness of the results. Based on these findings, we recommend regular reporting of stability metrics to enhance the scientific rigor of interpretability studies.

Takeaways, Limitations

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Takeaways:
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Interpretability methodologies, especially circuit discovery, should be considered statistical estimates, and variance and robustness analyses are essential.
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State-of-the-art circuit discovery methodologies such as EAP-IG exhibit high structural variance and hyperparameter sensitivity, raising questions about the stability of their results.
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Regular reporting of stability metrics is needed to enhance the reliability of interpretability studies.
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Limitations:
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We focus on stability analysis for a specific circuit discovery methodology (EAP-IG), and generalization to other interpretability methodologies may be limited.
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The types of controlled perturbations may be limited and may not fully reflect the robustness of real-world environments.
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No specific guidelines or standards have been proposed for reporting stability metrics.
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