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Large language models (LLMs) have triggered tremendous success to empower our daily life by generative information.
2002
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N. Kandpal, E. Wallace , et al. , “Deduplicating training data mitigates privacy risks in language models,” in Proc. ICML 2022 , Baltimore, USA, Jun. 2022
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E. J. Hu, Y. Shen , et al. , “LoRA: Low-rank adaptation of large language models,” in Proc. ICLR 2022 , Virtual Edition, Jan. 2022
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2023
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2023
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J. Zhang, S. Vahidian , et al. , “Towards building the federated GPT: Federated instruction tuning,” in International Workshop on Federated Learning in the Age of Foundation Models in Conjunction with NeurIPS 2023 , New Orleans, LA, USA, Dec. 2023
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2023
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2023
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Y. Wang, Y. Kordi , et al. , “Self-instruct: Aligning language model with self generated instructions,” in Proc. ACL 2023 , Toronto, Canada, Jul. 2023
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J. Zhu, R. Li , et al. , “AoI-based temporal attention graph neural network for popularity prediction and content caching,” IEEE Trans. Cogn. Commun. Netw. , vol. 9, no. 2, pp. 345–358, 2023
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J. Su, M. Ahmed , et al. , “RoFormer: Enhanced transformer with rotary position embedding,” Neurocomputing , vol. 568, p. 127063, 2024
2024
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R. Taori, I. Gulrajani , et al. , “Stanford alpaca: An instruction-following llama model,” https://github.com/tatsu-lab/stanford_alpaca
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P. Liu, W. Yuan , et al. , “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, pp. 195:1–195:35, Jan. 2023
2023
Cited alongside, same era.
2024
Closest in time.