In the face of uncertainty, the ability to *seek information* is of fundamental importance. In many practical applications, such as medical diagnosis and troubleshooting, the information needed to solve the task is not initially given and has to be actively sought by asking follow-up questions (for example, a doctor asking a patient for more details about their symptoms). In this work, we introduce Uncertainty of Thoughts (UoT), an algorithm to augment large language models with the ability to actively seek information by asking effective questions. UoT combines 1) an *uncertainty-aware simulation approach* which enables the model to simulate possible future scenarios and how likely they are to occur, 2) *uncertainty-based rewards* motivated by information gain which incentivizes the model to seek information, and 3) a *reward propagation scheme* to select the optimal question to ask in a way that maximizes the expected reward. In experiments on medical diagnosis, troubleshooting, and the `20 Questions` game, UoT achieves an average performance improvement of 38.1% in the rate of successful task completion across multiple LLMs compared with direct prompting and also improves efficiency (i.e., the number of questions needed to complete the task). Our code has been released [here](https://github.com/zhiyuanhubj/UoT)

翻译：面对不确定性时，*寻求信息*的能力具有根本重要性。在许多实际应用中（如医疗诊断和故障排除），完成任务所需的信息最初并未给出，必须通过提出后续问题来主动获取（例如医生询问患者更多症状细节）。本研究提出不确定性思维（UoT）算法，通过赋予大型语言模型主动提出有效问题的信息寻求能力。UoT融合了三大核心机制：1）*不确定性感知模拟方法*，使模型能够模拟未来可能场景及其发生概率；2）基于信息增益理论的*不确定性奖励机制*，激励模型主动获取信息；3）*奖励传播方案*，以最大化期望奖励为目标选择最优提问策略。在医疗诊断、故障排除和“20个问题”游戏的实验中，相较于直接提示方法，UoT在多个LLM上实现了任务平均成功率38.1%的提升，同时提高了任务完成效率（即所需提问次数）。我们的代码已发布于[此处](https://github.com/zhiyuanhubj/UoT)