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An emerging method to cheaply improve a weaker language model is to finetune it on outputs from a stronger model, such as a proprietary system like ChatGPT (e.g., Alpaca, Self-Instruct, and others).
Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2014
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Stealing machine learning models via prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Knockoff nets: Stealing functionality of black-box models
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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A framework for the extraction of deep neural networks by leveraging public data
Soham Pal, Yash Gupta, Aditya Shukla, Aditya Kanade, Shirish Shevade, and Vinod Ganapathy · 2019
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PRADA: protecting against DNN model stealing attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal, and N Asokan · 2019
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DAWN: Dynamic adversarial watermarking of neural networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal, and N Asokan · 2019
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Imitation attacks and defenses for black-box machine translation systems
Eric Wallace, Mitchell Stern, and Dawn Song · 2020
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RealToxicityPrompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith · 2020
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Thieves on sesame street! Model extraction of BERT-based APIs
Kalpesh Krishna, Gaurav Singh Tomar, Ankur P Parikh, Nicolas Papernot, and Mohit Iyyer · 2020
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Prediction poisoning: Towards defenses against DNN model stealing attacks
Tribhuvanesh Orekondy, Bernt Schiele, and Mario Fritz · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Xiaodong Song, and Jacob Steinhardt · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Behavior cloning is miscalibrated
Leo Gao · 2021
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Dataset inference: Ownership resolution in machine learning
Pratyush Maini, Mohammad Yaghini, and Nicolas Papernot · 2021
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ChatGPT: Optimizing language models for dialogue., 2022
OpenAI · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al · 2022
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Dylan Patel and Afzal Ahmad · 2023
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren · 2023
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister · 2023
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Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing · 2023
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Koala: A dialogue model for academic research
Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song · 2023
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Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick · 2022
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PaLM: Scaling language modeling with pathways
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Constitutional AI: Harmlessness from AI feedback
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An important next step on our AI journey
Sundar Pichai · 2023
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AnthropicAI · 2023
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How close is ChatGPT to human experts? Comparison corpus, evaluation, and detection
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Reinforcement learning for language models
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Visual instruction tuning
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mPLUG-Owl: Modularization empowers large language models with multimodality
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