cs.CYMar 6, 2026

Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks

Authors: Shira Gur-Arieh, Angelina Wang, Sina Fazelpour

Organizations: Harvard University, United States · Cornell Tech, United States · Northeastern University, United States

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Abstract

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.

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Figure 1. Drivers of ambiguity collapse across the pipeline. A schematic showing drivers of ambiguity collapse at multiple layers of the pipeline (data/training, model objectives, interface, and deployment), with arrows indicating how pressures at each layer push toward premature meaning-settlement.
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