Graph Intelligence with Large Language Models and Prompt Learning
Author:
Affiliation:
1. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
2. The Chinese University of Hong Kong, HongKong, China
Funder
NSFC
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671456
Reference109 articles.
1. Iz Beltagy Kyle Lo et al. 2019. SciBERT: A pretrained language model for scientific text. arXiv preprint arXiv:1903.10676 (2019).
2. William Brannon Suyash Fulay et al. 2023. ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings. arXiv preprint arXiv:2305.14321 (2023).
3. Language models are few-shot learners;Brown Tom;NeurIPS,2020
4. He Cao Zijing Liu et al. 2023. InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery. arXiv preprint arXiv:2311.16208 (2023).
5. Ziwei Chai Tianjie Zhang et al. 2023. GraphLLM: Boosting Graph Reasoning Ability of Large Language Model. arXiv preprint arXiv:2310.05845 (2023).
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