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cs.AI2026
Harnessing Textual Refusal Directions for Multimodal Safety
Moreno D'Incà, Nicu Sebe, Massimiliano Mancini
To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. Both strategies are less feas…
cs.AI2025
MLLMs are Deeply Affected by Modality Bias
Xu Zheng, Chenfei Liao, Yuqian Fu +15
Recent advances in Multimodal Large Language Models (MLLMs) have shown promising results in integrating diverse modalities such as texts and images. MLLMs are heavily influenced by…