Natural Language is All a Graph Needs

Anonymous

Abstract: The emergence of large-scale pre-trained language models, such as ChatGPT, has revolutionized various research fields in artificial intelligence. Transformers-based large language models (LLMs) have gradually replaced CNNs and RNNs to unify fields of computer vision and natural language processing. Compared with the data that exists relatively independently such as images, videos or texts, graph is a type of data that contains rich structural and relational information. Meanwhile, natural language, as one of the most expressive mediums, excels in describing complex structures. However, existing work on incorporating graph learning problems into the generative language modeling framework remains very limited. As the importance of LLMs continues to grow, it becomes essential to explore whether LLMs can also replace GNNs as the foundation model for graphs. In this paper, we propose InstructGLM (Instruction-finetuned Graph Language Model), systematically design highly scalable prompts based on natural language instructions, and use natural language to describe the geometric structure and node features of the graph for instruction tuning an LLM to perform learning and inference on graphs in a generative manner. Our method exceeds all competitive GNN baselines on ogbn-arxiv, Cora and PubMed datasets, which demonstrates the effectiveness of our method and sheds light on generative large language models as the foundation model for graph machine learning. Our code will be released once published.

Paper Type: long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Contribution Types: NLP engineering experiment

Languages Studied: English

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