Large Language Model Guided Tree-of-Thought

Original Paper: https://arxiv.org/abs/2305.08291

By: Jieyi Long

Abstract:

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{

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Summary Notes

Enhancing Problem-Solving with the Tree-of-Thought Framework

The introduction of large language models (LLMs) like GPT-4 has been a game-changer in artificial intelligence, showing exceptional skills in tasks requiring short-range reasoning.

However, their performance in complex, long-term problem-solving is often limited due to their inability to check the accuracy of each solution step.

This limitation affects their use in more complicated tasks and raises concerns about their dependability.

To address this, we present the Tree-of-Thought (ToT) framework, a new method aimed at improving the problem-solving abilities of LLMs.

Overview of the Tree-of-Thought Framework

The ToT framework consists of four main parts:

• Prompter Agent: Directs the LLM's thinking.
• Checker Module: Ensures each step is correct.
• Memory Module: Keeps track of steps for possible backtracking.
• ToT Controller: Decides the direction of the thought process.

This approach uses a tree-like structure for exploring solutions, allowing for backtracking if needed, thus significantly boosting the LLM's problem-solving skills, especially for complex tasks.

Example: Solving Sudoku

An example of the ToT framework in action is solving Sudoku puzzles. The process includes:

1. Giving the LLM a partially completed puzzle.
2. Using the LLM's suggestions to fill in more spaces, checking each step, and deciding whether to continue or backtrack as advised.

This method has shown to be more effective than traditional LLM techniques in solving puzzles.

Related Works

While the ToT framework enhances LLM capabilities, it distinguishes itself with its unique structured backtracking and multi-step verification, building on but also improving past approaches aimed at boosting LLM reasoning abilities.

System Architecture

The components of the ToT framework work as follows:

• Checker Module: Uses either rule-based or neural network-based systems, ideal for tasks with clear rules.
• Memory Module: Logs reasoning steps, aiding in backtrack.
• ToT Controller: Makes decisions on whether to backtrack or proceed, navigating through the solution space effectively.

This setup allows for adjustments in problem-solving based on the results of each step, closely mimicking how humans solve problems.

Evaluation

Tests on Sudoku puzzles showed that ToT significantly outperforms standard LLM methods, especially in complex puzzles, proving its efficiency in handling multi-step reasoning.

Discussion and Future Work

Though ToT marks a significant advancement, its reliance on rule-based systems for some tasks may limit its wider application.

Future work could involve exploring neural network-based controllers and incorporating advanced reinforcement learning to improve decision-making and versatility.

Additionally, self-play techniques could lead ToT to discover new problem-solving strategies.

Conclusion

The Tree-of-Thought framework significantly enhances the problem-solving abilities of LLMs by introducing step-by-step verification and correction mechanisms.

It not only emulates human problem-solving methods but also widens the scope for applying LLMs to complex situations.

With ongoing refinements, the possibilities for AI problem-solving improvements are vast, setting the stage for more reliable and adaptable AI systems in the future.