In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when it comes to the graph learning tasks, existing LLMs present very serious flaws due to their several inherited weaknesses in performing {multi-step logic reasoning}, {precise mathematical calculation} and {perception about the spatial and temporal factors}. To address such challenges, in this paper, we will investigate the principles, methodologies and algorithms to empower existing LLMs with graph reasoning ability, which will have tremendous impacts on the current research of both LLMs and graph learning. Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer (Graph Reasoning oriented Toolformer) framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools. Specifically, we will investigate to teach Graph-ToolFormer to handle various graph data reasoning tasks in this paper, including both (1) very basic graph data loading and graph property reasoning tasks, ranging from simple graph order and size to the graph diameter and periphery, and (2) more advanced reasoning tasks on real-world graph data, such as bibliographic networks, protein molecules, sequential recommender systems, social networks and knowledge graphs.

翻译：本文旨在开发一种能够对复杂图数据进行推理的大语言模型。目前，大语言模型在各类自然语言学习任务上已取得令人瞩目的性能，其扩展也被应用于研究多模态数据的视觉任务。然而，在面对图学习任务时，现有大语言模型由于在{多步逻辑推理}、{精确数学计算}和{时空因素感知}方面存在固有缺陷，表现出严重的不足。为解决这些挑战，本文将探究赋予现有大语言模型图推理能力的原理、方法和算法，这将对当前大语言模型与图学习的研究产生重大影响。受最新ChatGPT和Toolformer模型的启发，我们提出Graph-ToolFormer（面向图推理的Toolformer）框架，通过ChatGPT增强的提示教大语言模型自身使用外部图推理API工具。具体而言，本文将研究如何教Graph-ToolFormer处理各类图数据推理任务，包括：(1)基础图数据加载与图属性推理任务，从简单的图阶和图大小到图的直径和外围结构；(2)真实世界图数据上的高阶推理任务，例如文献网络、蛋白质分子、序列推荐系统、社交网络和知识图谱。