arXiv AI
Emergent Misalignment Is Not Magical
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jun 5
In-Training Defenses against Emergent Misalignment in Language Models
arXiv:2508. 06249v3 Announce Type: replace Abstract: Fine-tuning lets practitioners repurpose aligned large language models (LLMs) for new domains, yet recent work reveals emergent misalignment (EM): Even a small, domain-specific fine-tune can induce harmful behaviors far outside the target domain.
arXiv Computation and Language
Aug 25
On the Threat Model of Weird Generalization and Emergent Misalignment
arXiv:2608.23476v1 Announce Type: new Abstract: Narrow fine-tuning on small, domain-specific datasets can produce broad and surprising changes in model behavior-a phenomenon called weird generalizati...
arXiv AI
Sep 30
The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment
arXiv:2609.37914v1 Announce Type: cross Abstract: Fine-tuning large language models on narrow, misaligned tasks can undo their post-training alignment and induce novel misaligned behaviors -- a pheno...
Hugging Face Trending Papers
Aug 11
Data Attribution of Emergent Misalignment with Persona Features
Emergent misalignment (EM) is the phenomenon where fine-tuning a language model on a narrow task leads to harmful behavior in unrelated domains. A leading mechanistic account attributes EM to persona features: latent directions acquired during pre-training that misaligned fine-tuning amplifies.
arXiv Computation and Language
Sep 15
Data Attribution of Emergent Misalignment with Persona Features
arXiv:2608.11025v2 Announce Type: replace Abstract: Emergent misalignment (EM) is the phenomenon where fine-tuning a language model on a narrow task leads to harmful behavior in unrelated domains. A...
arXiv AI
Oct 2
Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs
arXiv:2610.01428v1 Announce Type: cross Abstract: Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed...