Large Language Model Guided Tree-of-Thought
Jieyi Long
In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel
approach aimed at improving the problem-solving capabilities of auto-regressive
large language models (LLMs). The ToT technique is inspired by the human mind's
approach for solving complex reasoning tasks through trial and error. In this
process, the human mind explores the solution space through a tree-like thought
process, allowing for backtracking when necessary. To implement ToT as a
software system, we augment an LLM with additional modules including a prompter
agent, a checker module, a memory module, and a ToT controller. In order to
solve a given problem, these modules engage in a multi-round conversation with
the LLM. The memory module records the conversation and state history of the
problem solving process, which allows the system to backtrack to the previous
steps of the thought-process and explore other directions from there. To verify
the effectiveness of the proposed technique, we implemented a ToT-based solver
for the Sudoku Puzzle. Experimental results show that the ToT framework can
significantly increase the success rate of Sudoku puzzle solving. Our
implementation of the ToT-based Sudoku solver is available on GitHub:
\url{https://github.com/jieyilong/tree-of-thought-puzzle-solver}.