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The application of large language models (LLMs) to graph data has attracted a lot of attention recently.
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Deep learning on graphs: A survey
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The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . 3045–3059
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Chain-of-thought prompting elicits reasoning in large language models
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A frustratingly simple approach improves textual graph learning
K Duan, Q Liu, TS Chua, S Yan, WT Ooi, Q Xie, and J Simteg He. 2023 · 2023
Exploring the potential of large language models (llms) in learning on graphs
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, et al · 2024
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In-context learning for extreme multi-label classification
Karel D’Oosterlinck, Omar Khattab, François Remy, Thomas Demeester, Chris Develder, and Christopher Potts. 2024 · 2024
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Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
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Talk like a Graph: Encoding Graphs for Large Language Models. In The Twelfth International Conference on Learning Representations
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. 2024 · 2024
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A survey on graph representation learning methods
Shima Khoshraftar and Aijun An. 2024 · 2024
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llama.cpp: LLM inference in C/C++
Georgi Gerganov. 2023 · 2023
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Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, and Bryan Hooi. 2023 · 2023
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DSPy: Compiling declarative language model calls into self-improving pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al · 2023
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Efficient large language models fine-tuning on graphs
Rui Xue, Xipeng Shen, Ruozhou Yu, and Xiaorui Liu. 2023 · 2023
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Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2023 · 2023
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Empower text-attributed graphs learning with large language models (llms)
Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, and Xuecang Zhang. 2023 · 2023
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https://huggingface.co/bartowski/Phi-3.5-mini-instruct-GGUF ,
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Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024 · 2024
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Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, and Omar Khattab. 2024 · 2024
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Let your graph do the talking: Encoding structured data for llms
Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, and Jonathan Halcrow. 2024 · 2024
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Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov. 2024 · 2024
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Efficient tuning and inference for large language models on textual graphs
Yun Zhu, Yaoke Wang, Haizhou Shi, and Siliang Tang. 2024 · 2024
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