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TopicsMechanistic Interpretability LLM Steering Sparse Autoencoders

Ranking SAE features by absolute steering effect at large intervention scales surfaces reward-model-bias features in studied demonstrations.

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Ranking features by absolute steering effect |SEα(f ; C|P )| with a large intervention scale effectively surfaces RM bias features for demonstrations of both train and test biases. However, due to the need to pick non-obvious hyperparameters (taking absolute value and using a large intervention size), we are uncertain whether using this technique with the same hyperparameters will transfer to other settings.

Auditing language models for hidden objectivespreprintextraction 0.65

Marks, Samuel et al. · arXiv.org · Mar 14, 2025 · original

Section 5.3.2 identifying SAE features that causally mediate behaviors

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