AI RESEARCH
Cat-DPO: Category-Adaptive Safety Alignment
arXiv CS.CL
•
ArXi:2604.17299v1 Announce Type: new Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones. Most preference-based safety alignment methods collapse safety into a single scalar that is applied uniformly to every preference pair. The result is a model that looks safe on average but stays relatively unsafe on a minority of harm categories.