  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)
