  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.
