Alignment

SAVeS: Steering Safety Judgments in Vision-Language Models via Semantic Cues

We study how semantic cues can be used to steer safety judgments in vision-language models, proposing SAVeS as a method to improve the reliability of safety-relevant decisions made by multimodal models.