Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering
Value steering should change an LLM's normative priorities while preserving the scenario, facts, and task constraints underlying its answer. Conventional activation edits often change both. We introduce an editable semantic-value interface on frozen residual states, with a one-way semantic-to-value pathway that grounds value recognition in context. Stop-gradient blocks feedback through this pathway; swap consistency, topic de-confounding, and decorrelation encourage selective codes. At inference, editing the value code produces a residual delta while holding the semantic code fixed. On two instruction-tuned backbones, this interface improves semantic preservation and reduces benign refusa...
| Authors | Jiale Dai, Hongcan Deng, Liuxian Ma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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