Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration
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Abstract (arXiv)
When a language model refuses to answer a prompt, it is unclear whether the correct answer is erased from its internal representations, or merely suppressed at the output layer. We investigate this mechanism using a controlled withhold setting, which yields perfectly matched answering and refusal trajectories for bidirectional activation patching. We uncover a causal asymmetry in intervention locality under matched causal interventions, which we term broken symmetry. Even when a model generates a clean refusal, the correct answer remains linearly recoverable from its hidden states. Furthermore, releasing this withheld answer is a highly local operation, requiring only a single-position patch. Conversely, the reverse operation is not equally local: reimposing suppression requires broader interventions across multiple positions, and assembling a coherent refusal sequence is more difficult still. We further demonstrate that while an average answer-to-refusal displacement vector marks the geometric difference between these states, it fails to act as a reliable, reversible linear control toggle between behaviours. Taken together, our findings show that refusal does not function as a simple symmetric switch. For safety and auditing, this implies that probe recoverability can overestimate true behavioural control, and locating refusal-relevant directions does not reliably grant the ability to steer a model from answering to coherent refusal.
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References · 21
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
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- 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
- 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,009
- 2024Refusal in Language Models Is Mediated by a Single Directioncited by 960
- 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
- 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
- 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 328
- 2020Information-Theoretic Probing with Minimum Description Lengthcited by 230
- 2020Information-Theoretic Probing for Linguistic Structurecited by 204
- 2024How to use and interpret activation patchingcited by 191
- 2023Mitigating the Alignment Tax of RLHFcited by 181
9 more
- 2024A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicitycited by 179
- 2020A Theory of Usable Information Under Computational Constraintscited by 153
- 2023Future Lens: Anticipating Subsequent Tokens from a Single Hidden Statecited by 116
- 2025The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independencecited by 104
- 2021Conditional probing: measuring usable information beyond a baselinecited by 61
- 2025Understanding Refusal in Language Models with Sparse Autoencoderscited by 26
- 2025Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuningcited by 22
- 2026The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimationcited by 4
- 2026Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Typescited by 3
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