Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities

ArXi:2603.10396v1 Announce Type: new Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertainty framework. This mismatch leads to systematic failure modes, particularly in settings that involve ambiguous question-answering, in-context learning, and self-reflection.