ISSN :2582-9793

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

Original Research (Published On: 21-Mar-2024 )
Graph-of-Thought: Utilizing Large Language Models to Solve Complex and Dynamic Business Problems
DOI : https://dx.doi.org/10.54364/AAIML.2024.41118

Julia Li

Adv. Artif. Intell. Mach. Learn., 4 (1):2077-209

Julia Li : Microsoft

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DOI: https://dx.doi.org/10.54364/AAIML.2024.41118

Article History: Received on: 02-Jan-24, Accepted on: 14-Mar-24, Published on: 21-Mar-24

Corresponding Author: Julia Li

Email: jull@microsoft.com

Citation: Ye Li (2024). Graph-of-Thought: Utilizing Large Language Models to Solve Complex and Dynamic Business Problems. Adv. Artif. Intell. Mach. Learn., 4 (1 ):2077-209


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.


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