Language models can cause harm through rhetorical misalignment, study finds

A new research paper introduces the concept of "rhetorical misalignment" in language models, where the way information is presented can lead to suboptimal human decisions. An experiment using medical licensing exam data showed that LLMs induced an average of 2.81% harmful decision flips among participants. These revisions were linked to cognitive biases like anchoring and loss aversion, highlighting a safety concern where models can be factually correct but still cause harm through their language. AI

IMPACT Highlights a novel safety concern where LLM output phrasing can induce cognitive biases and lead to harmful decisions, even when factually accurate.

RANK_REASON Academic paper detailing a new phenomenon and experimental findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• anchoring
• arXiv
• authority bias
• Hugging Face
• Language Models
• loss aversion
• United States Medical Licensing Examination

• paper
• safety

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