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The emergence of foundation models, including language and vision models, has reshaped AI's landscape, offering capabilities across various applications.
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Accelerating training of transformer-based language models with progressive layer dropping
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Finetuned language models are zero-shot learners
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Liu, H. et al · 2022
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FedPETuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models
Zhang, Z. et al · 2023
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FedPrompt: Communication-efficient and privacy-preserving prompt tuning in federated learning
Zhao, H., Du, W., Li, F., Li, P. & Liu, G · 2023
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Towards building the federated gpt: Federated instruction tuning
Zhang, J. et al · 2023
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Towards a unified view of parameter-efficient transfer learning
He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T. & Neubig, G · 2021
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LoRA: Low-rank adaptation of large language models
Hu, E. J. et al · 2021
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Learning how to ask: Querying LMs with mixtures of soft prompts
Qin, G. & Eisner, J · 2021
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The power of scale for parameter-efficient prompt tuning
Lester, B., Al-Rfou, R. & Constant, N · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L. & Liang, P · 2021
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P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Liu, X. et al · 2021
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PromptFL: Let federated participants cooperatively learn prompts instead of models-federated learning in age of foundation model
Guo, T., Guo, S., Wang, J., Tang, X. & Xu, W · 2023
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FedAdapter: Efficient federated learning for modern NLP
Cai, D., Wu, Y., Wang, S., Lin, F. X. & Xu, M · 2023
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SmoothQuant: Accurate and efficient post-training quantization for large language models
Xiao, G. et al · 2023
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Offsite-tuning: Transfer learning without full model
Xiao, G., Lin, J. & Han, S · 2023
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DC-CCL: Device-cloud collaborative controlled learning for large vision models
Ding, Y. et al · 2023
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Kuang, W. et al · 2023
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On the effect of dropping layers of pre-trained transformer models
Sajjad, H., Dalvi, F., Durrani, N. & Nakov, P · 2023
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FedPETuning: When federated learning meets the parameter-efficient tuning methods of pre-trained language models
Zhang, Z. et al · 2023
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Towards building the federated GPT: Federated instruction tuning
Zhang, J. et al · 2023
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