AIBearisharXiv – CS AI · Mar 96/10
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Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks
Researchers have identified 'ambiguity collapse' as a significant epistemic risk when large language models encounter ambiguous terms and produce singular interpretations without human deliberation. The phenomenon threatens decision-making processes in content moderation, hiring, and AI self-regulation by bypassing normal human practices of meaning negotiation and potentially distorting shared vocabularies over time.