TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment 事件

TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment arXiv:2606.01755v1 Announce Type: cross Abstract: Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsistencies across social groups, where some groups systematically receive less accurate responses on objective tasks. Existing alignment methods either ignore personalization or mainly focus on subjective preference alignment,