SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics

SaFeR-Steer is a novel framework designed to improve the safety alignment of Multi-modal Large Language Models (MLLMs) in multi-turn dialogues, addressing challenges like escalating unsafe intent and long-context safety decay. It employs synthetic bootstrapping and feedback dynamics, while also releasing the STEER dataset for training and evaluation.