Fetching the paper…

Reading the bibliography…

2023

Integrating Graphs with Large Language Models: Methods and Prospects

Pan, Shirui, Zheng, Yizhen, Liu, Yixin

Original
PDF
Post on X

Understand

Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more.

Parallelly, graph-structured data, an intrinsic data type, is pervasive in real-world scenarios.
Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest.
This paper bifurcates such integrations into two predominant categories.

Integrating Graphs with Large Language Models: Methods and Prospects · Around

Built on

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al., “Chain-of-thought prompting elicits reasoning in large language models,” Advances in Neural Information Processing Systems
2022
Earlier work this paper cites.
M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, L. Gianinazzi, J. Gajda, T. Lehmann, M. Podstawski, H. Niewiadomski, P. Nyczyk, et al., “Graph of thoughts: Solving elaborate problems with large language models,” arXiv:2308.09687
Original
2023
Earlier work this paper cites.
B. Lei, C. Liao, C. Ding, et al., “Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought,” arXiv:2308.08614
Original
2023
Earlier work this paper cites.
Z. Chen, H. Mao, H. Li, W. Jin, H. Wen, X. Wei, S. Wang, D. Yin, W. Fan, H. Liu, et al., “Exploring the potential of large language models (LLMs) in learning on graphs,” arXiv:2307.03393
Original
2023
Earlier work this paper cites.

Similar

X. He, X. Bresson, T. Laurent, B. Hooi, et al., “Explanations as Features: LLM-Based Features for Text-Attributed Graphs,” arXiv:2305.19523
Original
2023
Cited alongside, same era.
J. Guo, L. Du, H. Liu, “GPT4Graph: Can Large Language Models Understand Graph Structured Data? An Empirical Evaluation and Benchmarking,” arXiv:2305.15066
Original
2023
Cited alongside, same era.
S. Yao, D. Yu, J. Zhao, I. Shafran, T.L. Griffiths, Y. Cao, K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” arXiv:2305.10601
Original
2023
Cited alongside, same era.

Then

R. Ye, C. Zhang, R. Wang, S. Xu, Y. Zhang, et al., “Natural language is all a graph needs,” arXiv:2308.07134
Original
2023
Closest in time.
S. Hong, X. Zheng, J. Chen, Y. Cheng, C. Zhang, Z. Wang, S. K. Yau, Z. Lin, L. Zhou, C. Ran, et al., “Metagpt: Meta programming for multi-agent collaborative framework,” arXiv:2308.00352
Original
2023
Closest in time.
Z. Chen, L. N. Zheng, C. Lu, J. Yuan, D. Zhu, et al., “ChatGPT Informed Graph Neural Network for Stock Movement Prediction,” in SIGKDD 2023 Workshop on Robust NLP for Finance
2023
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…