Large language models (LLMs) are increasingly used to make sense of ambiguous, open-textured, value-laden terms. Platforms routinely rely on LLMs for content moderation, asking them to label text based on disputed concepts like "hate speech" or "incitement"; hiring managers may use LLMs to rank who counts as "qualified"; and AI labs increasingly train models to self-regulate under constitutional-style ambiguous principles such as "biased" or "legitimate". This paper introduces ambiguity collapse: a phenomenon that occurs when an LLM encounters a term that genuinely admits multiple legitimate interpretations, yet produces a singular resolution, in ways that bypass the human practices through which meaning is ordinarily negotiated, contested, and justified. Drawing on interdisciplinary accounts of ambiguity as a productive epistemic resource, we develop a taxonomy of the epistemic risks posed by ambiguity collapse at three levels: process (foreclosing opportunities to deliberate, develop cognitive skills, and shape contested terms), output (distorting the concepts and reasons agents act upon), and ecosystem (reshaping shared vocabularies, interpretive norms, and how concepts evolve over time). We illustrate these risks through three case studies, and conclude by sketching multi-layer mitigation principles spanning training, institutional deployment design, interface affordances, and the management of underspecified prompts, with the goal of designing systems that surface, preserve, and responsibly govern ambiguity.

翻译：大语言模型（LLMs）正日益被用于理解具有模糊性、开放结构和价值负载的术语。平台通常依赖LLMs进行内容审核，要求其基于"仇恨言论"或"煽动性内容"等争议性概念对文本进行分类；招聘经理可能使用LLMs来评估谁符合"合格"标准；人工智能实验室越来越多地训练模型在"存在偏见"或"合法"等宪法式模糊原则下进行自我调节。本文提出模糊性坍缩现象：当LLM遇到一个确实允许多种合理解释的术语时，却产生单一确定性判断，这种方式绕过了人类通常通过协商、争论和论证来构建意义的实践过程。借鉴跨学科研究中将模糊性视为有益认知资源的观点，我们构建了一个关于模糊性坍缩在三个层面引发的认知风险分类体系：过程层面（剥夺了审议机会、认知技能发展及争议术语塑造的可能性）、输出层面（扭曲了行为主体所依据的概念和理由）以及生态系统层面（重塑共享词汇、解释规范及概念的历时演化方式）。我们通过三个案例研究阐明这些风险，最后提出涵盖训练过程、机构部署设计、界面功能支持以及未充分定义提示管理的多层次缓解原则，旨在设计能够呈现、保留并负责任地管理模糊性的系统。