Linear Probe Interpretability. We train simple linear residual stream probes on the (in-doma

We train simple linear residual stream probes on the (in-domain) training dataset we also use for finding the SAE features. Jul 2, 2025 · We show that linear probes can separate real-world evaluation and deployment prompts, suggesting that current models internally represent this distinction. Therefore, The choice of probe model carries implications: Linear Probes: A logistic regression or linear Support Vector Machine (SVM) is often used. at [46]. However, their decomposition includes negative concepts, reducin interpretability, and uses task-specific concept dictionaries. Model Interpretability: Probing classifiers help shed light on how complex machine learning models represent and process different linguistic aspects. In addition to the generalizing networks trained on correct data, two types of intentionally flawed models are used for Dec 17, 2025 · Alright so I've been messing around with LLMs for a few weeks now. We could see how well they perform at predicting whether a photo was taken in or outdoors, for example. The results of the sparse local linear models trained for two instances with different predicted classes are shown in Figure 14. We also find that current safety evaluations are correctly classified by the probes, suggesting that they already appear artificial or inauthentic to models.

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