AIBearisharXiv – CS AI · 7h ago7/10
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Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity
A research study reveals that large language models are significantly more susceptible to being misled by peer consensus than they are at correcting their own errors, posing critical risks for multi-agent AI systems. The findings show that authority labels and social pressure drive harmful revisions without improvement from reasoning interventions like chain-of-thought prompting.