Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning
cs.CL, cs.AI
Submitted: 2026-10-02
Updated: 2026-10-02
Comments: 55 pages, 5 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- gpt-oss-120b & gpt-oss-20b Model Card
- ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)
- STaR-GATE: Teaching Language Models to Ask Clarifying Questions
- Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets
- Generative Social Choice
- Bayesian Preference Elicitation with Language Models
- Language Models (Mostly) Know What They Know
- CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models
- Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
- Knowing but Not Showing: LLMs Recognize Ambiguity but Rarely Ask Clarifying Questions
- Structured Uncertainty guided Clarification for LLM Agents
- One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models
- Instruction-Following Evaluation for Large Language Models
- PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
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