mlsanityData to 14 Oct 2026

What Is a Clarification? Estimating Model-Dependent Input Ambiguity

arXiv 2610.20052 ↗First posted 14 Oct 2026cs.CLcited by 0

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Abstract (arXiv)

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 open-domain QA and executable Text-to-SQL, circleclar achieves strong recovery of these answer sets. In QA, the recovered support yields the strongest overall performance in 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 29 references that are in the map. First posted on arXiv 14 Oct 2026.

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References · 22

  1. 2020AmbigQA: Answering Ambiguous Open-domain Questionscited by 390
  2. 2023We're Afraid Language Models Aren't Modeling Ambiguitycited by 126
  3. 2023Decomposing Uncertainty for Large Language Models through Input Clarification Ensemblingcited by 114
  4. 2023Clarify When Necessary: Resolving Ambiguity Through Interaction with LMscited by 98
  5. 2024Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questionscited by 76
  6. 2023Selectively Answering Ambiguous Questionscited by 66
  7. 2024Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Trainingcited by 57
  8. 2024Aligning Language Models to Explicitly Handle Ambiguitycited by 56
  9. 2024CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Modelscited by 54
  10. 2023Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Modelscited by 43
  11. 2024AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queriescited by 35
  12. 2023Benchmarking and Improving Text-to-SQL Generation under Ambiguitycited by 34
10 more
  1. 2025Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agentscited by 32
  2. 2023Asking Clarification Questions to Handle Ambiguity in Open-Domain QAcited by 26
  3. 2025The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguitycited by 17
  4. 2025Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsingcited by 13
  5. 2025Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approachcited by 10
  6. 2025Reasoning about Intent for Ambiguous Requestscited by 4
  7. 2026Task-Awareness Improves LLM Generations and Uncertaintycited by 3
  8. 2026Disentangling Ambiguity from Instability in Large Language Models: A Clinical Text-to-SQL Case Studycited by 3
  9. 2026Knowing but Not Showing: LLMs Recognize Ambiguity but Rarely Ask Clarifying Questionscited by 3
  10. 2026Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generationcited by 1

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