Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information
  Seeking in Large Language Models
Zhiyuan Hu Chumin Liu Xidong Feng Yilun Zhao See-Kiong Ng Anh Tuan Luu Junxian He Pang Wei Koh Bryan Hooi
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 57.8%
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).