SMoT: Think in State Machine

New approach uses State Machine of Thought (SMoT) and expert knowledge to improve language model reasoning accuracy.

Major Takeaways

1. SMoT Paradigm: The State Machine of Thought (SMoT) paradigm leverages pre-existing knowledge in the form of predefined state machines to guide Large Language Models (LLMs) in effective problem-solving.

2. Multi-Agent Mechanism: SMoT employs a multi-agent mechanism to delegate different objectives to different agents, enhancing the reasoning accuracy of the LLM.

3. Performance Improvement: Experimental results demonstrate that SMoT outperforms state-of-the-art baseline methods, achieving significant improvements in accuracy and efficiency, particularly in array reasoning and classical reinforcement learning tasks.

Introduction

In recent years, advancements in large language models (LLMs) have prompted various research topics aiming to unlock their full potential and enhance their problem-solving abilities. While existing approaches, such as Chain-of-Thoughts (CoT), have shown effectiveness, they sometimes struggle with complex problems, leading to the proposed State Machine of Thought (SMoT) paradigm.

Related Work

• Task Decomposition and Planning: Previous research has explored prompting LLMs to decompose complex problems into subtasks gradually completing them via chains-of-thought prompting, and in-domain planners have been utilized for designing effective plans for domain-specific tasks.

• Reflection and Refinement: Existing LLMs possess strong capabilities for task planning, with researchers proposing reflection mechanisms and utilizing external feedback for correct evaluation of current reasoning paths.

State Machine of Thoughts

• The Design of LLM-driven State Machines: SMoT incorporates LLM thinking in state machines and involves state definition and state transition optimization to enhance reasoning accuracy.

• Planning Agent and Action Agent: SMoT utilizes a division of labor between Planning Agent (PlAgt) and Action Agent (ActAgt) to break down complex sequential problems into discrete state transitions.

Comparison with Existing Prompting Approaches

• Comparison: SMoT significantly outperforms existing prompting approaches such as CoT, CoT-SC, ToT, and GoT, particularly in accuracy and efficiency for various reasoning tasks.

Example Use Cases

• The Greatest Sum Divisible by Three: SMoT effectively solves this array reasoning task, showcasing the successful implementation of the paradigm.

• Taxi: SMoT outperforms CoT and ToT methods in a classical reinforcement learning task, demonstrating superior accuracy and efficiency.

Experiments

• Performance: SMoT outperforms baselines in determining the greatest sum divisible by three and successfully navigates the taxi in challenging scenarios with high accuracy and efficiency.

Limitations

• Limitations: SMoT has limitations in handling problems that do not involve state transitions, faces challenges in parallel partitioning of the reasoning process, and requires manual design of state machines.

Critique

The article effectively introduces the novel SMoT paradigm and demonstrates its effectiveness through experiments. However, it would benefit from a more detailed comparison with other state-of-the-art methods, potential real-world applications, and a discussion on meta-learning or transfer learning aspects.

Overall, the paper provides valuable insights into leveraging pre-existing knowledge for guiding LLM reasoning and presents a promising approach in enhancing problem-solving capabilities. However, addressing the limitations and exploring broader applications would add depth to the paper.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-26
Abstract	http://arxiv.org/abs/2312.17445v1
HTML	https://browse.arxiv.org/html/2312.17445v1
Truncated	False
Word Count	11018