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Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely constrained to English and narrow audio domains. Prior benchmarks typically focus on a single audio modality, i.e., speech, sound, or music, limiting the systematic investiga...
This paper comprehensively studies effective prompt injection attacks against 14 widely used open-source and three closed-source LLMs on five attack benchmarks and proposes a straightforward and effective hypnotism attack, showing that this attack causes aligned language models to generate objectionable behaviors.
The causes of modal divergence are probed, offering insights into fostering culturally robust MLLMs, and a Multilingual, Multimodal Alignment framework for Cultural grounding evaluation is proposed.
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