Tree of Uncertain Thoughts Reasoning for Large Language Models
Shentong Mo Miao Xin
While the recently introduced Tree of Thoughts (ToT) has heralded
advancements in allowing Large Language Models (LLMs) to reason through
foresight and backtracking for global decision-making, it has overlooked the
inherent local uncertainties in intermediate decision points or "thoughts".
These local uncertainties, intrinsic to LLMs given their potential for diverse
responses, remain a significant concern in the reasoning process. Addressing
this pivotal gap, we introduce the Tree of Uncertain Thoughts (TouT) - a
reasoning framework tailored for LLMs. Our TouT effectively leverages Monte
Carlo Dropout to quantify uncertainty scores associated with LLMs' diverse
local responses at these intermediate steps. By marrying this local uncertainty
quantification with global search algorithms, TouT enhances the model's
precision in response generation. We substantiate our approach with rigorous
experiments on two demanding planning tasks: Game of 24 and Mini Crosswords.
The empirical evidence underscores TouT's superiority over both ToT and
chain-of-thought prompting methods.