AI Research
Statistically Grounded Sparse-Feature Interventions for Activation-Space Control in Large Language Models
Activation steering offers a lightweight alternative to fine-tuning for behavioral control of large language models, but SAE-based steering methods often rely on learned steering objectives or single-criterion feature selection. We introduce a transparent SAE-feature steering pip...
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ArXiv CS.AI
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ArXiv CS.AI
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This research introduces a more statistically robust and transparent method for controlling Large Language Model behavior without costly fine-tuning. It highlights the crucial need to evaluate not just desired behavioral shifts but also the preservation of output quality, a gap often overlooked in current activation steering research.
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