Generative multimodal models can exhibit safety failures that are inherently relational: two benign concepts can become unsafe when linked by a specific action or relation (e.g., child-drinking-wine). Existing unlearning and concept-erasure approaches often target isolated concepts or image-text pairs, which can cause collateral damage to benign uses of the same objects and relations. We propose relationship-aware safety unlearning: a framework that explicitly represents unsafe object-relation-object (O-R-O) tuples and applies targeted parameter-efficient edits (LoRA) to suppress unsafe tuples while preserving object marginals and safe neighboring relations. We include CLIP-based experiments and robustness evaluation under paraphrase, contextual, and out-of-distribution image attacks.

翻译：生成式多模态模型可能表现出本质上是关系性的安全失效：两个良性概念在通过特定动作或关系连接时（例如，儿童-饮用-葡萄酒）可能变得不安全。现有的遗忘学习和概念擦除方法通常针对孤立概念或图像-文本对，这可能导致对同一对象和关系的良性使用产生附带损害。我们提出关系感知的安全遗忘学习框架，该框架显式表示不安全的物体-关系-物体（O-R-O）元组，并应用针对性的参数高效编辑（LoRA）来抑制不安全元组，同时保留对象边际分布和安全的相邻关系。我们包含基于CLIP的实验，并在释义、上下文以及分布外图像攻击下进行鲁棒性评估。