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In fine-tuning large language models (LLMs), conserving computational resources while maintaining effectiveness and improving outcomes within the same computational constraints is crucial.
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Attention is all you need
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Gpt-3: Its nature, scope, limits, and consequences
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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh · 2020
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Lora: Low-rank adaptation of large language models
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
X. L. Li and P. Liang · 2021
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B. Newman, P. K. Choubey, and N. Rajani · 2021
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Improving the efficiency of transformers for resource-constrained devices
Qlora: Efficient finetuning of quantized llms
T. Dettmers, A. Pagnoni, A. Holtzman, and L. Zettlemoyer · 2023
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Parameter-efficient fine-tuning of large-scale pre-trained language models
N. Ding, Y. Qin, G. Yang, F. Wei, Z. Yang, Y. Su, S. Hu, Y. Chen, C.-M. Chan, W. Chen, et al · 2023
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Make your pre-trained model reversible: From parameter to memory efficient fine-tuning
B. Liao, S. Tan, and C. Monz · 2023
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Gpt understands, too
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang · 2023
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G. Pu, A. Jain, J. Yin, and R. Kaplan · 2023
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H. Tabani, A. Balasubramaniam, S. Marzban, E. Arani, and B. Zonooz · 2021
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Finetuned language models are zero-shot learners
J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le · 2021
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Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction
X. Chen, N. Zhang, X. Xie, S. Deng, Y. Yao, C. Tan, F. Huang, L. Si, and H. Chen · 2022
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M. Valipour, M. Rezagholizadeh, I. Kobyzev, and A. Ghodsi · 2022
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Towards parameter-efficient automation of data wrangling tasks with prefix-tuning
D. Vos, T. Döhmen, and S. Schelter · 2022
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J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
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Lora ensembles for large language model fine-tuning
X. Wang, L. Aitchison, and M. Rudolph
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Aligning large language models with human: A survey
Y. Wang, W. Zhong, L. Li, F. Mi, X. Zeng, W. Huang, L. Shang, X. Jiang, and Q. Liu
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Stanford alpaca: An instruction-following llama model
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto · 2023
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Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
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A survey of large language models
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al · 2023
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Text sentiment analysis based on llama models
P. Ji · 2024
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Gpt-4 as evaluator: Evaluating large language models on pest management in agriculture
S. Yang, Z. Yuan, S. Li, R. Peng, K. Liu, and P. Yang · 2024
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