让大模型像人一样聪明选信息，低成本高效决策。
The Curious Language Model: Strategic Test-Time Information Acquisition
- 用贪心树搜索估算每步行动的信息收益，动态选最优动作。
- 在临床诊断模拟中，准确率高于基线策略，且成本更低。
- 适合需要边推理边查信息的智能系统，如医疗辅助决策。
决策者常因信息不足而难以做出自信判断。此时可通过咨询专家或实验等方式获取信息，但不同信息获取方式成本各异，如何选择既有效又经济的行动成为关键挑战。本文提出 CuriosiTree，一种基于启发式、适用于大语言模型（LLM）零样本场景下的测试时信息获取策略。该方法通过贪心树搜索估算各行动的期望信息增益，并综合权衡预期信息量与成本，实现策略性行动选择。在临床诊断模拟中，CuriosiTree 能够高效整合异构信息源，其行动序列选择显著优于基线策略，提升诊断准确性的同时控制成本。
原文摘要 · Abstract (English)
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.
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