Thoughts的不确定性：不确定性感知规划增强大型语言模型的信息寻求能力
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
AI总结：
提出Uncertainty of Thoughts (UoT)算法，通过不确定性感知模拟、基于不确定性的奖励和奖励传播机制，增强大型语言模型主动提问寻求信息的能力，在医疗诊断等任务中平均成功率提升38.1%。
摘要：
面对不确定性时，*寻求信息*的能力具有根本性的重要性。在医疗诊断和故障排除等许多实际应用中，解决任务所需的信息并非初始给定，必须通过提出后续问题来主动寻求（例如，医生询问患者更多关于症状的细节）。在这项工作中，我们引入了Uncertainty of Thoughts (UoT)，这是一种通过提出有效问题来增强大型语言模型主动寻求信息能力的算法。UoT结合了：1）一种*不确定性感知模拟方法*，使模型能够模拟可能的未来场景及其发生的可能性；2）由信息增益驱动的*基于不确定性的奖励*，激励模型寻求信息；3）一种*奖励传播机制*，以最大化预期奖励的方式选择提出的最优问题。在医疗诊断、故障排除和“20个问题”游戏的实验中，与直接提示相比，UoT在多个LLM上的任务成功率平均提升了38.1%，并提高了效率（即完成任务所需的问题数量）。我们的代码已发布[在此](https://github.com/zhiyuanhubj/UoT)。
英文摘要：
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)
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
