Demystifying and Resolving the Pathologies of Linguistic Uncertainty in Large Language Models

Across diverse languages, speakers naturally convey uncertainty through epistemic hedges before asserting claims; however, uncertainty quantification (UQ) in large language models (LLMs) relies almost exclusively on post-hoc numerical scores. Can LLMs learn human-like, verbalized uncertainty? To investigate this, we systematically analyze model behavior along two primary axes: confidence placement (pre- vs. post-assertion) and modality (numeric scores vs. linguistic hedges). Our analysis uncovers two critical pathologies: (i) pre-assertion confidence acts as an ungrounded prior that is statistically decoupled from the downstream assertion; and (ii) linguistic hedging is acutely brittle under prompt paraphrasing. Consequently, naive epistemic hedging severely degrades model reliability. To remedy these deficiencies, we propose Stratified, a framework that decomposes generation into distinct epistemic and linguistic tiers by first committing to a statement and its numeric confidence before verbalizing the hedged assertion. Furthermore, to elicit fine-grained hedging across long-form responses, we construct WikiEU, a benchmark spanning diverse, factually verifiable queries, alongside an optimization recipe combining confidence-balanced supervised fine-tuning and reinforcement learning with a gated reward mechanism. Across three benchmarks and two models, our approach substantially improves verbalized uncertainty expression without sacrificing factual fidelity.