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Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future.
2018
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2021
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2023
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ITU-R, “IMT-2030 Framework and Overall Objectives of the Future Development of IMT for 2030 and Beyond,” https://www.itu.int/rec/R-REC-M.2160 , 2023
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Nvidia, “Fastertransformer,” 2023. [Online]. Available: https://github.com/NVIDIA/FasterTransformer
2023
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Y. Leviathan, M. Kalman, and Y. Matias, “Fast Inference From Transformers via Speculative Decoding,” in Proc. ICML , 2023
2023
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Y. Shen, J. Shao, X. Zhang, Z. Lin, H. Pan, D. Li, J. Zhang, and K. B. Letaief, “Large Language Models Empowered Autonomous Edge AI for Connected Intelligence,” IEEE Commun. Mag. , Jan. 2024
2024
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K. Alizadeh, S. I. Mirzadeh, D. Belenko, S. Khatamifard, M. Cho, C. C. Del Mundo, M. Rastegari, and M. Farajtabar, “LLM in a Flash: Efficient Large Language Model Inference with Limited Memory,” in Proc. ACL , Jul. 2024
2024
Cited alongside, same era.
Z. Lin, G. Qu, X. Chen, and K. Huang, “Split Learning in 6G Edge Networks,” IEEE Wireless Commun. , Jan. 2024
2024
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2024
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2024
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2024
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2024
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Z. Hao, H. Jiang, S. Jiang, J. Ren, and T. Cao, “Hybrid SLM and LLM for edge-Cloud Collaborative Inference,” in Proceedings of the Workshop on Edge and Mobile Foundation Models , 2024, pp. 36–41
2024
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