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Toward understanding and preventing misalignment generalization
Anthropic researchers identify an internal feature that drives misalignment generalization in language models and show it can be reversed with minimal fine-tuning.
We study how training on incorrect responses can cause broader misalignment in language models and identify an internal feature driving this behavior—one that can be reversed with minimal fine-tuning.
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