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The rapid evolution of network technologies and the growing complexity of network tasks necessitate a paradigm shift in how networks are designed, configured, and managed.
A. S. Jacobs, R. J. Pfitscher, R. H. Ribeiro, R. A. Ferreira, L. Z. Granville, and S. G. Rao, “Deploying natural language intents with lumi,” in Proceedings of the ACM SIGCOMM Conference Posters and Demos , 2019, p. 82–84
2019
Earlier work this paper cites.
B. Tian, X. Zhang, E. Zhai, H. H. Liu, Q. Ye, C. Wang, X. Wu, Z. Ji, Y. Sang, M. Zhang et al. , “Safely and automatically updating in-network acl configurations with intent language,” in Proceedings of the ACM SIGCOMM Conference , 2019, pp. 214–226
2019
Earlier work this paper cites.
Y. Ouyang, C. Yang, Y. Song, X. Mi, and M. Guizani, “A brief survey and implementation on refinement for intent-driven networking,” IEEE Network , vol. 35, no. 6, pp. 75–83, 2021
2021
Earlier work this paper cites.
H. Chen, Y. Miao, L. Chen, H. Sun, H. Xu, L. Liu, G. Zhang, and W. Wang, “Software-defined network assimilation: Bridging the last mile towards centralized network configuration management with nassim,” in Proceedings of the ACM SIGCOMM Conference , 2022, p. 281–297
2022
Earlier work this paper cites.
Y. Song, C. Yang, J. Zhang, X. Mi, and D. Niyato, “Full-life cycle intent-driven network verification: Challenges and approaches,” IEEE Network , pp. 1–8, 2022
2022
Earlier work this paper cites.
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 , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.
D. Gao, L. Ji, L. Zhou, K. Q. Lin, J. Chen, Z. Fan, and M. Z. Shou, “AssistGPT: A general multi-modal assistant that can plan, execute, inspect, and learn,” in arXiv 2306.08640 , 2023
2023
Cited alongside, same era.
L. Bariah, Q. Zhao, H. Zou, Y. Tian, F. Bader, and M. Debbah, “Large language models for telecom: The next big thing?” in arXiv 2306.10249 , 2023
2023
Cited alongside, same era.
Y. Huang, M. Xu, X. Zhang, D. Niyato, Z. Xiong, S. Wang, and T. Huang, “AI-generated network design: A diffusion model-based learning approach,” IEEE Network , pp. 1–1, 2023
2023
Cited alongside, same era.
A. Maatouk, F. Ayed, N. Piovesan, A. D. Domenico, M. Debbah, and Z.-Q. Luo, “Teleqna: A benchmark dataset to assess large language models telecommunications knowledge,” in arXiv 2310.15051 , 2023
2023
Closest in time.
Y. Miao, Y. Bai, L. Chen, D. Li, H. Sun, X. Wang, Z. Luo, Y. Ren, D. Sun, X. Xu, Q. Zhang, C. Xiang, and X. Li, “An empirical study of netops capability of pre-trained large language models,” in arXiv 2309.05557 , 2023
2023
Closest in time.
M. Xu, P. Huang, W. Yu, S. Liu, X. Zhang, Y. Niu, T. Zhang, F. Xia, J. Tan, and D. Zhao, “Creative robot tool use with large language models,” in arXiv 2310.13065 , 2023
2023
Closest in time.
J. Wang, Y. Liang, F. Meng, Z. Sun, H. Shi, Z. Li, J. Xu, J. Qu, and J. Zhou, “Is ChatGPT a good NLG evaluator? a preliminary study,” in arXiv 2303.04048 , 2023
2023
Closest in time.
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Y. Chen, R. Li, Z. Zhao, C. Peng, J. Wu, E. Hossain, and H. Zhang, “Netgpt: A native-ai network architecture beyond provisioning personalized generative services,” in arXiv 2307.06148 , 2023
2023
Cited alongside, same era.
H. Lee, S. Phatale, H. Mansoor, K. Lu, T. Mesnard, C. Bishop, V. Carbune, and A. Rastogi, “RLAIF: Scaling reinforcement learning from human feedback with AI feedback,” in arXiv 2309.00267 , 2023
2023
Closest in time.