Rational Clarification by Assistive Agents via Value-of-Information Reasoning
cs.AI, cs.CL, cs.HC, cs.MA
Submitted: 2026-09-29
Updated: 2026-09-29
Comments: 54 pages, 11 figures. Under review
License: http://creativecommons.org/licenses/by/4.0/
The gist: Users of language-based assistive agents often make ambiguous requests.
Terminology
Abstract
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --- or ask a clarifying question. Which option is the most safe and helpful? A common approach is to ask questions that minimize uncertainty about the user's intent until a threshold is reached. However, this neglects the impact of uncertainty reduction on downstream performance, the costs of asking versus acting immediately, and the possibility that users may provide corrections without being asked. To navigate these trade-offs, we introduce Rational Enquiry via Value-of-Information Reasoning (REVOIR). REVOIR makes clarification decisions via inference-time reasoning about the value-of-information of a question, which captures the expected improvement in task reward due to the answer received. In two assistive tasks --- ambiguous question answering (CondAmbigQA) and preference-aligned household task planning (ADAPT) --- we show that REVOIR achieves greater success with fewer questions than approaches based on prompting, chain-of-thought, fine-tuning, or information gain, improving preference satisfaction on ADAPT by 13-15% over a fine-tuned clarification policy while requiring no training and asking five times fewer questions. Furthermore, when the assistant can receive cheap user corrections after acting, REVOIR naturally infers that asking questions is not always efficient, demonstrating the adaptivity of our approach. In contrast, we find that vanilla reasoning agents fail to adaptively clarify user requests, and request fewer clarifications as reasoning effort increases.
Sources
- Optimal Particle-based Approximation of Discrete Distributions (OPAD)
- STaR-GATE: Teaching Language Models to Ask Clarifying Questions
- $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval
- Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like People
- Bayesian Preference Elicitation with Language Models
- MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning
- Flexible Agent Alignment with Goal Inference from Open-Ended Dialog
- ADAPT: Actively Discovering and Adapting to Preferences for any Task
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