A challenge in deploying large language models is distinguishing between uncertainty due to task variability and model limitations. Input ambiguity, where multiple interpretations are plausible, is a key source of aleatoric uncertainty.
Existing methods estimate this uncertainty by generating multiple clarifications and answers, but answers can be redundant, costly, and potentially misleading. The new approach bypasses answer generation, instead directly analyzing the space of plausible interpretations.
This clarification-only method improves ambiguity detection metrics, reduces computational costs significantly, and produces estimates with lower correlation to epistemic uncertainty. It offers a more efficient way to assess input ambiguity in language tasks.
This development matters for engineers managing models and agents, as it enables more accurate and resource-efficient uncertainty estimation based on interpretation space rather than response analysis.
Source: https://arxiv.org/abs/2609.04543