Hugging Face Trending Papers
GAPS: Dimension-Level Gates for Conditional Activation Steering
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arXiv Computation and Language
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GAPS: Dimension-Level Gates for Conditional Activation Steering
The paper introduces GAPS, a dimension‑level gating approach for activation steering in language models. GAPS uses two training‑free gates—a static separability gate based on AUROC and a dynamic posterior gate based on a Gaussian model—to selectively apply steering vectors only to neurons that carry reliable concept information or are currently mis‑activated. Experiments on Gemma‑3 and Qwen‑3 show that GAPS improves or matches the performance of token‑level methods, notably reducing Gemma‑3’s toxicity rate from 6.52% to 0.48% under a fixed capability budget.
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Hugging Face Trending Papers
Jul 21
Mark, Don't Erase: Token Inoculation for Dual-Use Knowledge in LLMs
Safety interventions on dual-use knowledge typically choose between destroying hazardous content (e. g.