Che, Tianshi, Liu, Ji, Zhou, Yang, Ren, Jiaxiang, Zhou, Jiwen, Sheng, Victor, Dai, Huaiyu, and Dou, Dejing. Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization. Retrieved from https://par.nsf.gov/biblio/10538069. Web. doi:10.18653/v1/2023.emnlp-main.488.

Che, Tianshi, Liu, Ji, Zhou, Yang, Ren, Jiaxiang, Zhou, Jiwen, Sheng, Victor, Dai, Huaiyu, & Dou, Dejing. Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization. Retrieved from https://par.nsf.gov/biblio/10538069. https://doi.org/10.18653/v1/2023.emnlp-main.488

Che, Tianshi, Liu, Ji, Zhou, Yang, Ren, Jiaxiang, Zhou, Jiwen, Sheng, Victor, Dai, Huaiyu, and Dou, Dejing. "Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization". Country unknown/Code not available: Proceedings of the 28th Conference on Empirical Methods in Natural Language Processing (EMNLP'23). https://doi.org/10.18653/v1/2023.emnlp-main.488. https://par.nsf.gov/biblio/10538069.

@article{osti_10538069,
place = {Country unknown/Code not available}, title = {Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization}, url = {https://par.nsf.gov/biblio/10538069}, DOI = {10.18653/v1/2023.emnlp-main.488}, abstractNote = {}, journal = {}, publisher = {Proceedings of the 28th Conference on Empirical Methods in Natural Language Processing (EMNLP'23)}, author = {Che, Tianshi and Liu, Ji and Zhou, Yang and Ren, Jiaxiang and Zhou, Jiwen and Sheng, Victor and Dai, Huaiyu and Dou, Dejing}, }