SOURCE-LINKED INTELLIGENCE
From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs
A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the model's knowledge. In language tasks, a central source of such aleatoric uncertainty is input ambiguity or underspecification, where multiple interpretations remain plausible. Existing decomposition methods estimate aleatoric uncertainty by generating multiple clarifications of the input, querying the model for an answer under each clarification, and comparing the resulting answers. We argue that answers are not necessary for identifying ambigui
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T23:00:52.000Z
First collected: 2026-09-21T04:31:57.454Z. This is not the publication date.