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Recent advancements in fine-tuning proprietary language models enable customized applications across various domains but also introduce two major challenges: high resource demands and security risks.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Earlier work this paper cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
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.
A backdoor attack against lstm-based text classification systems
Jiazhu Dai, Chuanshuai Chen, and Yufeng Li · 2019
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Triton: an intermediate language and compiler for tiled neural network computations
Philippe Tillet, Hsiang-Tsung Kung, and David Cox · 2019
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Interpreting GPT: the logit lens
nostalgebraist · 2020
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Badpre: Task-agnostic backdoor attacks to pre-trained nlp foundation models
Kangjie Chen, Yuxian Meng, Xiaofei Sun, Shangwei Guo, Tianwei Zhang, Jiwei Li, and Chun Fan · 2021
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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On the robustness of backdoor-based watermarking in deep neural networks
Masoumeh Shafieinejad, Nils Lukas, Jiaqi Wang, Xinda Li, and Florian Kerschbaum · 2021
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Revisiting parameter-efficient tuning: Are we really there yet?
Guanzheng Chen, Fangyu Liu, Zaiqiao Meng, and Shangsong Liang · 2022
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Sslguard: A watermarking scheme for self-supervised learning pre-trained encoders
Tianshuo Cong, Xinlei He, and Yang Zhang · 2022
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Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
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Gptq: Accurate post-training quantization for generative pre-trained transformers
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2022
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Backdoor learning: A survey
Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Llama recipes: Examples to get started using the llama models from meta
Meta AI · 2023
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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al · 2023
Cited alongside, same era.
Longlora: Efficient fine-tuning of long-context large language models
Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, and Jiaya Jia · 2023
Cited alongside, same era.
From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy
Maanak Gupta, CharanKumar Akiri, Kshitiz Aryal, Eli Parker, and Lopamudra Praharaj · 2023
Cited alongside, same era.
Xinlei He, Savvas Zannettou, Yun Shen, and Yang Zhang · 2023
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2024
Closest in time.
Open llm leaderboard v2
Clémentine Fourrier, Nathan Habib, Alina Lozovskaya, Konrad Szafer, and Thomas Wolf · 2024
Closest in time.
Fine-tuning with the Gemini API
Google · 2024
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Decoding compressed trust: Scrutinizing the trustworthiness of efficient llms under compression
Junyuan Hong, Jinhao Duan, Chenhui Zhang, Zhangheng Li, Chulin Xie, Kelsey Lieberman, James Diffenderfer, Brian Bartoldson, Ajay Jaiswal, Kaidi Xu, et al · 2024
Closest in time.
Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2024
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Unfamiliar finetuning examples control how language models hallucinate
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Cited alongside, same era.
Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
Cited alongside, same era.
Meet dan — the ‘jailbreak’ version of chatgpt and how to use it — ai unchained and unfiltered
Michael King · 2023
Cited alongside, same era.
Plateform for GPT Fine-tuning
OpenAI · 2023
Cited alongside, same era.
Gpt-3.5 turbo fine-tuning and API updates, 8 2023a
Andrew Peng, Michael Wu, John Allard, Logan Kilpatrick, and Steven Heidel · 2023
Cited alongside, same era.
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2023
Cited alongside, same era.
S-lora: Serving thousands of concurrent lora adapters
Ying Sheng, Shiyi Cao, Dacheng Li, Coleman Hooper, Nicholas Lee, Shuo Yang, Christopher Chou, Banghua Zhu, Lianmin Zheng, Kurt Keutzer, et al · 2023
Cited alongside, same era.
On the exploitability of instruction tuning
Manli Shu, Jiongxiao Wang, Chen Zhu, Jonas Geiping, Chaowei Xiao, and Tom Goldstein · 2023
Cited alongside, same era.
Katie Kang, Eric Wallace, Claire Tomlin, Aviral Kumar, and Sergey Levine · 2024
Closest in time.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee · 2024
Closest in time.
Bitdelta: Your fine-tune may only be worth one bit
James Liu, Guangxuan Xiao, Kai Li, Jason D Lee, Song Han, Tri Dao, and Tianle Cai · 2024
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Detecting hallucinations in large language model generation: A token probability approach, 2024
Ernesto Quevedo, Jorge Yero, Rachel Koerner, Pablo Rivas, and Tomas Cerny · 2024
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Trustllm: Trustworthiness in large language models
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, et al · 2024
Closest in time.
Peftguard: Detecting backdoor attacks against parameter-efficient fine-tuning, 2024
Zhen Sun, Tianshuo Cong, Yule Liu, Chenhao Lin, Xinlei He, Rongmao Chen, Xingshuo Han, and Xinyi Huang · 2024
Closest in time.
Defending llms against jailbreaking attacks via backtranslation
Yihan Wang, Zhouxing Shi, Andrew Bai, and Cho-Jui Hsieh · 2024
Closest in time.
Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt · 2024
Closest in time.
Measuring and reducing llm hallucination without gold-standard answers via expertise-weighting
Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo, Andrew Estornell, and Yang Liu · 2024
Closest in time.
Badchain: Backdoor chain-of-thought prompting for large language models
Zhen Xiang, Fengqing Jiang, Zidi Xiong, Bhaskar Ramasubramanian, Radha Poovendran, and Bo Li · 2024
Closest in time.
A comprehensive study of jailbreak attack versus defense for large language models, 2024
Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li, and Stjepan Picek · 2024
Closest in time.
Backdooring instruction-tuned large language models with virtual prompt injection
Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, and Hongxia Jin · 2024
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Jailbreak attacks and defenses against large language models: A survey, 2024
Sibo Yi, Yule Liu, Zhen Sun, Tianshuo Cong, Xinlei He, Jiaxing Song, Ke Xu, and Qi Li · 2024
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Shuai Zhao, Meihuizi Jia, Zhongliang Guo, Leilei Gan, Jie Fu, Yichao Feng, Fengjun Pan, and Luu Anh Tuan · 2024
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How alignment and jailbreak work: Explain llm safety through intermediate hidden states, 2024
Zhenhong Zhou, Haiyang Yu, Xinghua Zhang, Rongwu Xu, Fei Huang, and Yongbin Li · 2024
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