Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse research fields of artificial intelligence, especially for open-ended tasks. While the idea is less explored in the graph domain, despite the availability of numerous powerful graph models (GMs), they are restricted to tasks in a pre-defined form. Although several methods applying LLMs to graphs have been proposed, they fail to simultaneously handle the pre-defined and open-ended tasks, with LLM as a node feature enhancer or as a standalone predictor. To break this dilemma, we propose to bridge the pretrained GM and LLM by a Translator, named GraphTranslator, aiming to leverage GM to handle the pre-defined tasks effectively and utilize the extended interface of LLMs to offer various open-ended tasks for GM. To train such Translator, we propose a Producer capable of constructing the graph-text alignment data along node information, neighbor information and model information. By treating the node representation as a type of language, the proposed GraphTranslator empowers an LLM to make predictions based on node representation and language instructions, providing a unified perspective for both pre-defined and open-ended tasks. Extensive results show that the proposed GraphTranslator effectively improves the results of zero-shot node classification. The graph question answering experiments reveal our GraphTranslator potential across a broad spectrum of open-ended applications through language instructions.

翻译：大型语言模型（如ChatGPT）展现出强大的零样本和指令遵循能力，已引发人工智能各研究领域（尤其是开放式任务）的革命性变革。尽管存在大量强大的图模型（GM），但这一理念在图领域尚未得到充分探索——现有模型受限于预定义形式的任务。尽管已有多种将LLM应用于图的方法被提出，但无论是将LLM用作节点特征增强器还是独立预测器，它们均无法同时处理预定义任务与开放式任务。为突破这一困境，我们提出通过名为GraphTranslator的转换器桥接预训练GM与LLM，旨在利用GM高效处理预定义任务，同时借助LLM的扩展接口为GM提供各类开放式任务。为训练该转换器，我们设计了能够沿节点信息、邻居信息和模型信息构建图-文本对齐数据的生成器。通过将节点表征视为一种语言，所提出的GraphTranslator使LLM能够基于节点表征与语言指令进行预测，为预定义和开放式任务提供统一视角。大量实验结果表明，GraphTranslator有效提升了零样本节点分类的效果。图问答实验进一步揭示了我们的GraphTranslator通过语言指令在各类开放式应用中展现出的巨大潜力。