Bibliographic Details
| Title: |
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks |
| Authors: |
Zhang, Mengmei, Sun, Mingwei, Wang, Peng, Fan, Shen, Mo, Yanhu, Xu, Xiaoxiao, Liu, Hong, Yang, Cheng, Shi, Chuan |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Artificial Intelligence |
| More Details: |
Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, 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 translating node representation into tokens, GraphTranslator empowers an LLM to make predictions based on language instructions, providing a unified perspective for both pre-defined and open-ended tasks. Extensive results demonstrate the effectiveness of our proposed GraphTranslator on zero-shot node classification. The graph question answering experiments reveal our GraphTranslator potential across a broad spectrum of open-ended tasks through language instructions. Our code is available at: https://github.com/alibaba/GraphTranslator. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2402.07197 |
| Accession Number: |
edsarx.2402.07197 |
| Database: |
arXiv |
