On the Threat Model of Weird Generalization and Emergent Misalignment
Narrow fine-tuning on small, domain-specific datasets can produce broad and surprising changes in model behavior-a phenomenon called weird generalization (WG). Yet, it remains unclear what features of the fine-tuning data are necessary for WG to arise. Here, we address this question by investigating a range of plausibly relevant features, including dataset size, composition, language, presentation style, and novelty relative to a model's parametric knowledge. Further, since WG evaluations rely on small question sets that assess the extent of the generalization, we also analyze how sensitive th
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- PossiblePossibly related (embedding) · 46%Alignment →
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- LinkedLinked via arxiv author · 85%Mark Dredze →
“On the Threat Model of Weird Generalization and Emergent Misalignment”
- LinkedLinked via arxiv author · 85%Miriam Wanner →
“On the Threat Model of Weird Generalization and Emergent Misalignment”
- LinkedLinked via arxiv author · 85%William Walden →
“On the Threat Model of Weird Generalization and Emergent Misalignment”