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GraphGPT: Graph Instruction Tuning for Large Language Models
May 8, 2024, 4:48 a.m. | Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, Chao Huang
cs.CL updates on arXiv.org arxiv.org
Abstract: Graph Neural Networks (GNNs) have evolved to understand graph structures through recursive exchanges and aggregations among nodes. To enhance robustness, self-supervised learning (SSL) has become a vital tool for data augmentation. Traditional methods often depend on fine-tuning with task-specific labels, limiting their effectiveness when labeled data is scarce. Our research tackles this by advancing graph model generalization in zero-shot learning environments. Inspired by the success of large language models (LLMs), we aim to create a …
arxiv cs.ai cs.cl graph instruction tuning language language models large language large language models type
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