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Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence, exhibiting remarkable abilities to generalize across a wide array of tasks and applications beyond their initial training objectives.
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Graph few-shot learning with task-specific structures
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Context autoencoder for self-supervised representation learning
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Can Language Models Solve Graph Problems in Natural Language?
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Augmenting low-resource text classification with graph-grounded pre-training and prompting. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 506–516
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Harnessing the power of large language models for natural language to first-order logic translation
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Natural language is all a graph needs
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LLaGA: Large Language and Graph Assistant
Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah, and Zhangyang Wang. 2024 · 2024
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Universal prompt tuning for graph neural networks
Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2024 · 2024
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Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi. 2024a · 2024
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Yufei He, Zhenyu Hou, Yukuo Cen, Feng He, Xu Cheng, and Bryan Hooi. 2024b · 2024
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ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
Yuhan Li, Peisong Wang, Zhixun Li, Jeffrey Xu Yu, and Jia Li. 2024 · 2024
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FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
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Large language models can learn temporal reasoning
Siheng Xiong, Ali Payani, Ramana Kompella, and Faramarz Fekri. 2024 · 2024
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Testing AI Systems Leveraging Graph Perturbation. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering . 665–666
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Pre-training and prompting for few-shot node classification on text-attributed graphs. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4467–4478
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