Value of Information: A Framework for Human-Agent Communication

2026-01-10

arXiv.org

AI Summary

This work addresses the challenge faced by large language model agents in adaptively balancing the trade-offs between taking action and seeking clarification when handling ambiguous user requests—a problem exacerbated by existing approaches that either rely on task-specific hyperparameter tuning or ignore variations in risk. To overcome this limitation, the study introduces the theory of Value of Information (VoI) into human-AI interaction and proposes a parameter-free decision framework. During inference, the framework dynamically evaluates the expected utility gain from asking a question against the associated user cognitive cost, enabling context-aware, adaptive clarification strategies. By explicitly balancing task risk, query ambiguity, and user burden, the method matches or surpasses the best manually tuned baselines across four diverse tasks—20 Questions, medical diagnosis, flight booking, and e-commerce—with utility improvements of up to 1.36 points in high-risk scenarios.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Decision/Utility Theory

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Large Language Model (LLM) agents deployed for real-world tasks face a fundamental dilemma: user requests are underspecified, yet agents must decide whether to act on incomplete information or interrupt users for clarification. Existing approaches either rely on brittle confidence thresholds that require task-specific tuning, or fail to account for the varying stakes of different decisions. We introduce a decision-theoretic framework that resolves this trade-off through the Value of Information (VoI), enabling agents to dynamically weigh the expected utility gain from asking questions against the cognitive cost imposed on users. Our inference-time method requires no hyperparameter tuning and adapts seamlessly across contexts-from casual games to medical diagnosis. Experiments across four diverse domains (20 Questions, medical diagnosis, flight booking, and e-commerce) show that VoI consistently matches or exceeds the best manually-tuned baselines, achieving up to 1.36 utility points higher in high-cost settings. This work provides a parameter-free framework for adaptive agent communication that explicitly balances task risk, query ambiguity, and user effort.

Problem

• Value of Information
• Human-Agent Communication
• Large Language Model
• Decision-making under uncertainty
• User clarification

Innovation

• Value of Information
• Human-Agent Communication
• Decision-Theoretic Framework
• Large Language Models
• Adaptive Clarification