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With the advent and widespread deployment of Multimodal Large Language Models (MLLMs), the imperative to ensure their safety has become increasingly pronounced.
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Mao, C., Chiquier, M., Wang, H., Yang, J., Vondrick, C.: Adversarial Attacks Are Reversible With Natural Supervision. In: ICCV (2021)
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Carlini, N., Nasr, M., Choquette-Choo, C.A., Jagielski, M., Gao, I., Awadalla, A., Koh, P.W., Ippolito, D., Lee, K., Tramer, F., Schmidt, L.: Are aligned neural networks adversarially aligned? In: NeurIPS (2023)
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Cheng, X., Cao, B., Ye, Q., Zhu, Z., Li, H., Zou, Y.: Ml-lmcl: Mutual learning and large-margin contrastive learning for improving asr robustness in spoken language understanding. In: Proc. of ACL Findings (2023)
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Cheng, X., Zhu, Z., Cao, B., Ye, Q., Zou, Y.: Mrrl: Modifying the reference via reinforcement learning for non-autoregressive joint multiple intent detection and slot filling. In: Proc. of EMNLP Findings (2023)
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Li, J., Li, D., Savarese, S., Hoi, S.: BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In: ICML (2023)
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Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual Instruction Tuning. In: NeurIPS (2023)
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Meta: Llama usage policy (2023), https://ai.meta.com/llama/use-policy , accessed on 10-2023
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OpenAI: OpenAI usage policy (2023), https://openai.com/policies/usage-policies , accessed on 10-2023
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Schlarmann, C., Hein, M.: On the adversarial robustness of multi-modal foundation models. In: ICCV (2023)
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