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Recently, large language models (LLMs) have drawn extensive attention from academia and the public, due to the advent of the ChatGPT.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
Differentially private federated learning: A client level perspective
Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
Earlier work this paper cites.
Split learning for health: Distributed deep learning without sharing raw patient data, 2018
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Model inversion attacks against collaborative inference
Zecheng He, Tianwei Zhang, and Ruby B. Lee · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Information leakage in embedding models
Congzheng Song and Ananth Raghunathan · 2020
Cited alongside, same era.
Nopeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, and Ramesh Raskar · 2020
Cited alongside, same era.
Feature inference attack on model predictions in vertical federated learning
Xinjian Luo, Yuncheng Wu, Xiaokui Xiao, and Beng Chin Ooi · 2021
Cited alongside, same era.
Glm: General language model pretraining with autoregressive blank infilling
Long text and multi-table summarization: Dataset and method
Shuaiqi Liu, Jiannong Cao, Ruosong Yang, and Zhiyuan Wen · 2022
Later among the works it cites.
Introducing chatgpt
OpenAI · 2022
Later among the works it cites.
Glm-130b: An open bilingual pre-trained model
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, et al · 2022
Later among the works it cites.
Puma: Secure inference of llama-7b in five minutes
Ye Dong, Wen-jie Lu, Yancheng Zheng, Haoqi Wu, Derun Zhao, Jin Tan, Zhicong Huang, Cheng Hong, Tao Wei, and Wenguang Cheng · 2023
Closest in time.
Ciphergpt: Secure two-party gpt inference
Xiaoyang Hou, Jian Liu, Jingyu Li, Yuhan Li, Wen-jie Lu, Cheng Hong, and Kui Ren · 2023
Closest in time.
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Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang · 2022
Cited alongside, same era.
Unsplit: Data-oblivious model inversion, model stealing, and label inference attacks against split learning
Ege Erdogan, Alptekin Küpçü, and A. Ercüment Çiçek · 2022
Cited alongside, same era.
Iron: Private inference on transformers
Meng Hao, Hongwei Li, Hanxiao Chen, Pengzhi Xing, Guowen Xu, and Tianwei Zhang · 2022
Cited alongside, same era.
John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, and Alexander M. Rush · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.