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University of Michigan
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Deductive stereotyping in LLMs leads to biased reasoning, but targeted injection phrases can dramatically enhance fairness across diverse tasks.
A single biased example can completely undermine the alignment of large language models, revealing a critical flaw in their post-training safety mechanisms.
Current video MLLMs struggle to grasp fleeting visual events, with top models barely surpassing 39% accuracy on critical momentary tasks.
