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.

翻译：虽然最新提出的思维树（Tree of Thoughts，ToT）通过前瞻和回溯推理实现全局决策，为大语言模型带来了进步，但该方法忽略了中间决策点（即“思维”）中固有的局部不确定性。这些局部不确定性源于大语言模型生成多样化响应的潜在能力，在推理过程中始终是一个关键问题。针对这一重要空白，我们提出了不确定思维树（Tree of Uncertain Thoughts，TouT）——一种专为大语言模型定制的推理框架。我们的TouT有效利用蒙特卡洛丢弃法来量化大语言模型在中间步骤中多样化局部响应所对应的不确定性分数。通过将这种局部不确定性量化与全局搜索算法相结合，TouT提升了模型在响应生成中的精确度。我们通过两个具有挑战性的规划任务——“24点游戏”和“迷你填字游戏”——进行了严格实验来验证该方法。实验证据表明，TouT在性能上优于ToT和思维链提示方法。