Researchers have proposed a framework to validate whether human value geometry is preserved in the steering space of large language models, addressing a gap in activation steering research that typically validates on isolated behaviors. The work introduces a method to assess the alignment of value directions in the model's representation space with human-defined value dimensions, potentially improving the reliability of inference-time behavioral control for alignment-sensitive applications.
As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuning methods (e.g., RLHF, DPO) for behavioral control. However, existing work typically validates steering on isolated beh
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