Social bias in multi-agent debate amplifies LLM safety risks
Multi-agent debate in retrieval-augmented web apps causes unsafe adoption to surge from 0.0% to 100.0% under social pressure while silencing safe evidence across six risk categories.
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Multi-agent debate in retrieval-augmented web apps causes unsafe adoption to surge from 0.0% to 100.0% under social pressure while silencing safe evidence across six risk categories.
An OpenReview paper reports that large language models can develop novel social biases through adaptive exploration behavior.
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