mlsanityData to 7 Oct 2026

Measuring Alignment-Induced Activation Shifts Correctly: A Template-Controlled Difference-in-Differences Protocol

arXiv 2605.24583 ↗First posted 23 May 2026cs.LG cs.CL stat.MLcited by 0

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

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  3. 2017Deep reinforcement learning from human preferencescited by 3,846
  4. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  5. 2023Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!cited by 1,106
  6. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
  7. 2023Steering Language Models With Activation Engineeringcited by 824
  8. 2023The Linear Representation Hypothesis and the Geometry of Large Language Modelscited by 679
  9. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 673
  10. 2019Linear Mode Connectivity and the Lottery Ticket Hypothesiscited by 627
  11. 2018Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNscited by 585
  12. 2024Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainingcited by 478
12 more
  1. 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deepcited by 450
  2. 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 442
  3. 2024Alignment faking in large language modelscited by 316
  4. 2023LEACE: Perfect linear concept erasure in closed formcited by 254
  5. 2022The Alignment Problem from a Deep Learning Perspectivecited by 246
  6. 2023Linearity of Relation Decoding in Transformer Language Modelscited by 190
  7. 2023LoRA Fine-tuning Efficiently Undoes Safety Training in Llama 2-Chat 70Bcited by 183
  8. 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 179
  9. 2025Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMscited by 158
  10. 2025The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independencecited by 104
  11. 2024What Makes and Breaks Safety Fine-tuning? A Mechanistic Studycited by 54
  12. 2024Language Models Resist Alignment: Evidence From Data Compressioncited by 50

Built on by

Not cited yet.