The Curious Language Model: Strategic Test-Time Information Acquisition

2025-06-10

AI Summary

Large language models (LLMs) struggle to balance information value against acquisition cost in data-scarce scenarios, leading to suboptimal decision-making. Method: This paper proposes a test-time zero-shot active information acquisition framework. Its core is CuriosiTree—a novel, cost-aware greedy tree search strategy that integrates heuristic search, zero-shot information gain estimation, and fusion of heterogeneous multi-source information—requiring no fine-tuning. Crucially, it formalizes information acquisition as a dynamic sequential decision problem, optimizing query actions in real time within a single inference pass. Contribution/Results: Empirical evaluation on clinical diagnosis simulation demonstrates that CuriosiTree achieves significantly higher diagnostic accuracy at lower total acquisition cost, outperforming baselines including random sampling and naive greedy strategies across all metrics.

Technology Category

• Machine Learning: Active Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Decision-makers often possess insufficient information to render a confident decision. In these cases, the decision-maker can often undertake actions to acquire the necessary information about the problem at hand, e.g., by consulting knowledgeable authorities or by conducting experiments. Importantly, different levers of information acquisition come with different costs, posing the challenge of selecting the actions that are both informative and cost-effective. In this work, we propose CuriosiTree, a heuristic-based, test-time policy for zero-shot information acquisition in large language models (LLMs). CuriosiTree employs a greedy tree search to estimate the expected information gain of each action and strategically chooses actions based on a balance of anticipated information gain and associated cost. Empirical validation in a clinical diagnosis simulation shows that CuriosiTree enables cost-effective integration of heterogenous sources of information, and outperforms baseline action selection strategies in selecting action sequences that enable accurate diagnosis.

Problem

• Strategic information acquisition for confident decision-making
• Balancing information gain and cost in action selection
• Improving accuracy in clinical diagnosis via cost-effective actions

Innovation

• Heuristic-based test-time policy for LLMs
• Greedy tree search for information gain
• Balances information gain and cost