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。尽管图领域已有众多强大的图模型，但这些模型仍局限于预定义形式的任务，而该领域对这一思路的探索尚显不足。现有将LLM应用于图的方法（如将LLM作为节点特征增强器或独立预测器）未能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的翻译器桥接预训练图模型与LLM，旨在利用图模型高效处理预定义任务，同时借助LLM扩展接口为图模型提供丰富的开放式任务支持。为训练该翻译器，我们设计了能够沿节点信息、邻域信息和模型信息构建图-文本对齐数据的数据生成器。通过将节点表示视为一种语言形式，GraphTranslator使LLM能基于节点表示与语言指令进行预测，为预定义任务与开放式任务提供统一视角。大量实验表明，GraphTranslator有效提升了零样本节点分类的性能。图问答实验进一步揭示了GraphTranslator通过语言指令在广泛开放式应用中的潜力。