VLAF diagnostics show alignment faking is widespread in LLMs as small as 7B parameters, driven by consistent activation shifts that can be mitigated with contrastive steering vectors reducing faking by 58-94%.
While reporting the results, we aggregate the scores from each datapoint and normalize them to [0, 1]
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Value-Conflict Diagnostics Reveal Widespread Alignment Faking in Language Models
VLAF diagnostics show alignment faking is widespread in LLMs as small as 7B parameters, driven by consistent activation shifts that can be mitigated with contrastive steering vectors reducing faking by 58-94%.