Measuring Alignment-Induced Activation Shifts Correctly: A Template-Controlled Difference-in-Differences Protocol
Not on the map. Why?
Abstract (arXiv)
Comparing a model's internal activations before and after alignment is a natural way to ask what safety training changes: one forms the matrix of paired aligned-minus-base activations on safety-relevant inputs and reads off its effective rank or top direction. We show the obvious way to form this matrix is confounded. The aligned model is evaluated under a chat template the base model never saw, so the naive difference conflates the alignment shift with chat formatting. We introduce a four-variant decomposition of the modification matrix (naive, template-controlled, within-aligned, and difference-in-differences, DiD) that separates the two effects. Template control alone removes a 2.0-3.9x inflation of the measured effective rank across Llama-3.1-8B, Gemma-2-9B, and Qwen-2.5-7B; the DiD contrast is what recovers the refusal direction of Arditi et al. (2024), lifting its cosine alignment from 0.18-0.39 to 0.50-0.86. Projection-ablation across the three families confirms the recovered subspace is behaviorally active and that singular-value order is not causal order. We validate the protocol on a controlled testbed and distill it into measurement recommendations for activation-difference studies of alignment.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 26 references that are in the map. First posted on arXiv 23 May 2026.
Builds on
24 of 26 references.
References · 24
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
- 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,106
- 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
- 2023Steering Language Models With Activation Engineeringcited by 824
- 2023The Linear Representation Hypothesis and the Geometry of Large Language Modelscited by 679
- 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 673
- 2019Linear Mode Connectivity and the Lottery Ticket Hypothesiscited by 627
- 2018Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNscited by 585
- 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 478
12 more
- 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deepcited by 450
- 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 442
- 2024Alignment faking in large language modelscited by 316
- 2023LEACE: Perfect linear concept erasure in closed formcited by 254
- 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
- 2023Linearity of Relation Decoding in Transformer Language Modelscited by 190
- 2023LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70Bcited by 183
- 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 179
- 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMscited by 158
- 2025The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independencecited by 104
- 2024What Makes and Breaks Safety Fine-tuning? A Mechanistic Studycited by 54
- 2024Language Models Resist Alignment: Evidence From Data Compressioncited by 50
Built on by
Not cited yet.