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Shandong University
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Knowledge hallucinations are the most significant hurdle for MLLMs, revealing critical weaknesses in their robustness to false premises.
BRiG-AFA achieves up to 10.20 percentage points improvement in accuracy over greedy methods by leveraging a novel risk-to-go learning approach for active feature acquisition.
