What is a Clarification? Estimating Model-Dependent Input Ambiguity

Ambiguous inputs can admit multiple interpretations, each potentially yielding a different answer. Existing work largely treats this ambiguity as a property of the input, yet for a particular language model, only some alternatives may be known and answerable. We therefore distinguish world ambiguity, capturing all alternatives admitted by an input, from model ambiguity, capturing those accessible to the responding model. To characterize these alternatives, we formalize natural-language clarifications through three desiderata: uniqueness, faithfulness, and answer equivalence. We show that these desiderata induce a one-to-one correspondence between valid answers and equivalence classes of clarifications, implying that any interpretation can be represented either by its answer or by the clarifications that uniquely express it. We leverage this correspondence to introduce CIRCLECLAR, a black-box method that estimates the set of answers available to a model by checking consistency along an answer–clarification–answer cycle. Across QA, task instructions, and Text-to-SQL, CIRCLECLAR achieves strong recovery of these answer sets, yields the strongest overall performance on model-dependent ambiguity detection, and enables effective selection among answering, clarifying, and abstaining. Together, these results demonstrate the practical value of a model-dependent view of ambiguity and its characterization through an answer-clarification correspondence.