This work introduces Cross-Encoding Steering Evaluation, which freezes an intervention while re-encoding answers to the same held-out items, which favors extraction-index over semantic-label following on NormBank in three models.
Abstract
Activation steering is often evaluated under the answer encoding used to construct the direction. A reported gain may reflect the intended judgment or compatibility with answer identifiers seen during construction. We introduce Cross-Encoding Steering Evaluation, which freezes an intervention while re-encoding answers to the same held-out items. On NormBank, after A/B/C identifiers are reassigned, contrastive activation addition (CAA) induces larger target-versus-source score changes for the extraction indices than for the semantic labels under the new mapping. We call this extraction-index following. Varying identifier vocabulary (A/B/C, X/Y/Z, or 1/2/3) and row order shows that the effect tracks extraction index rather than row position. After matching direction norms across layers, extraction-index following emerges mainly at later depths. A low-rank output-sensitive component containing 15.4% of the direction's squared norm retains 96.3% of this effect. An Inference-Time Intervention (ITI)-style method also favors extraction-index over semantic-label following on NormBank in three models. In aggregate, MNLI favors extraction-index following, whereas Social Chemistry 101 (SC101) favors semantic-label following. Multiple-choice and open-ended evaluations can yield different behavioral conclusions. Thus, a steering gain under one answer encoding does not by itself identify what the intervention controls.
Activation steering controls model behavior by editing internal activations at inference time, and finds that a single MLP neuron is eval-correlated but not causal at both scales, and that scanning the real Pile yields a natural-text baseline competitive with the optimizer for the internal direction.
Attention rollout is used here as a diagnostic of attention-mediated propagation, not as a causal explanation or faithful attribution of the complete Transformer, but as a structural interpretation to the corresponding rollout propagation profile.
This work studies the stability of embedded steering for refusal suppression and brevity induction across five instruction-tuned models (3B-14B) under non-adversarial SFT and RLHF.
Philipp E. Glass, Allan Tucker, Yong-Min Li et al.· 1 citation
Findings provide evidence that truthfulness is a structured, linearly separable concept in the latent space of pretrained language models, and point toward interpretability-driven misinformation detection as a practical complement to retrieval-based pipelines.
P. Barcelos, Otávio Parraga, M. M. Delucis et al.· Lecture notes in computer sc...· 0 citations
irection patching is addressed with direction patching, a class-conditioned causal intervention applied across layers, token positions, and prompt formats to reframe the encoding-grounding gap as a problem of conditional transport in VLMs.
Steering Vector Dissection is proposed, a framework to explicitly isolate individual and semantically consistent features from these composite directions that tend to entangle multiple semantic and stylistic concepts into a single composite direction, leading to unpredictable steering effects.
Takeru Hiramatsu, Kyohei Atarashi, Koh Takeuchi et al.· 2 citations
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