English

Graph-of-Thought: Utilizing Large Language Models to Solve Complex and Dynamic Business Problems

Artificial Intelligence 2024-02-20 v2

Abstract

This paper presents Graph-of-Thought (GoT), a new model for workflow automation that enhances the flexibility and efficiency of Large Language Models (LLMs) in complex task execution. GoT advances beyond traditional linear and tree-like cognitive models with a graph structure that enables dynamic path selection. The open-source engine GoTFlow demonstrates the practical application of GoT, facilitating automated, data-driven decision-making across various domains. Despite challenges in complexity and transparency, GoTFlow's potential for improving business processes is significant, promising advancements in both efficiency and decision quality with continuous development.

Keywords

Cite

@article{arxiv.2401.06801,
  title  = {Graph-of-Thought: Utilizing Large Language Models to Solve Complex and Dynamic Business Problems},
  author = {Ye Li},
  journal= {arXiv preprint arXiv:2401.06801},
  year   = {2024}
}

Comments

Keywords: Graph-of-Thought (GoT), Workflow Automation, Large Language Models (LLMs), Task Execution, Data-Driven Decision Making, Complexity Management

R2 v1 2026-06-28T14:15:35.848Z