Large Language Models (LLMs) have achieved remarkable success in reasoning tasks with the development of prompting methods. However, existing prompting approaches cannot reuse insights of solving similar problems and suffer from accumulated errors in multi-step reasoning, since they prompt LLMs to reason \textit{from scratch}. To address these issues, we propose \textbf{\textit{Thought Propagation} (TP)}, which explores the analogous problems and leverages their solutions to enhance the complex reasoning ability of LLMs. These analogous problems are related to the input one, with reusable solutions and problem-solving strategies. Thus, it is promising to propagate insights of solving previous analogous problems to inspire new problem-solving. To achieve this, TP first prompts LLMs to propose and solve a set of analogous problems that are related to the input one. Then, TP reuses the results of analogous problems to directly yield a new solution or derive a knowledge-intensive plan for execution to amend the initial solution obtained from scratch. TP is compatible with existing prompting approaches, allowing plug-and-play generalization and enhancement in a wide range of tasks without much labor in task-specific prompt engineering. Experiments across three challenging tasks demonstrate TP enjoys a substantial improvement over the baselines by an average of 12\% absolute increase in finding the optimal solutions in Shortest-path Reasoning, 13\% improvement of human preference in Creative Writing, and 15\% enhancement in the task completion rate of LLM-Agent Planning.

翻译：大型语言模型（LLMs）在提示方法的发展下，于推理任务中取得了显著成功。然而，现有提示方法无法重复利用解决类似问题的洞察，且在多步推理中面临累积错误，因为它们提示LLMs“从零开始”推理。为解决这些问题，我们提出了**思想传播（TP）**，该方法探索类比问题并利用其解决方案来增强LLMs的复杂推理能力。这些类比问题与输入问题相关，具有可重用的解决方案和问题解决策略。因此，传播先前类比问题的解决洞察以启发新问题的解决具有前景。为实现这一点，TP首先提示LLMs提出并解决一组与输入问题相关的类比问题。然后，TP重用类比问题的结果，直接生成新解决方案或推导出知识密集型执行计划，以修正从零开始获得的初始解决方案。TP与现有提示方法兼容，可实现即插即用的泛化与增强，在广泛任务中无需大量特定于任务的提示工程。在三个挑战性任务上的实验表明，TP在最短路径推理中找到最优解的平均绝对提升为12%，创意写作中人类偏好提升13%，LLM智能体规划任务完成率提升15%，显著优于基线方法。