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Recent research indicates that large language models (LLMs) are susceptible to jailbreaking attacks that can generate harmful content.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
HotFlip: White-box adversarial examples for text classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2018
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Build it break it fix it for dialogue safety: Robustness from adversarial human attack
Emily Dinan, Samuel Humeau, Bharath Chintagunta, and Jason Weston · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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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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Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh · 2020
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Improving question answering model robustness with synthetic adversarial data generation
Max Bartolo, Tristan Thrush, Robin Jia, Sebastian Riedel, Pontus Stenetorp, and Douwe Kiela · 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
Earlier work this paper cites.
Gradient-based adversarial attacks against text transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, and Douwe Kiela · 2021
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Process for adapting language models to society (palms) with values-targeted datasets
Irene Solaiman and Christy Dennison · 2021
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Challenges in detoxifying language models
Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, and Po-Sen Huang · 2021
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Promptsource: An integrated development environment and repository for natural language prompts
Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, et al · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al · 2022
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al · 2022
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Biogpt: generative pre-trained transformer for biomedical text generation and mining
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu · 2022
Earlier work this paper cites.
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
Cited alongside, same era.
Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving · 2022
Cited alongside, same era.
Cold decoding: Energy-based constrained text generation with langevin dynamics
Lianhui Qin, Sean Welleck, Daniel Khashabi, and Yejin Choi · 2022
Cited alongside, same era.
Exploring the limits of domain-adaptive training for detoxifying large-scale language models
Boxin Wang, Wei Ping, Chaowei Xiao, Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Bo Li, Anima Anandkumar, and Bryan Catanzaro · 2022
Cited alongside, same era.
Self-instruct: Aligning language models with self-generated instructions
# instag: Instruction tagging for analyzing supervised fine-tuning of large language models
Keming Lu, Hongyi Yuan, Zheng Yuan, Runji Lin, Junyang Lin, Chuanqi Tan, Chang Zhou, and Jingren Zhou · 2023
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Analyzing leakage of personally identifiable information in language models
Nils Lukas, Ahmed Salem, Robert Sim, Shruti Tople, Lukas Wutschitz, and Santiago Zanella-Béguelin · 2023
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Black box adversarial prompting for foundation models
Natalie Maus, Patrick Chao, Eric Wong, and Jacob Gardner · 2023
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Tree of attacks: Jailbreaking black-box llms automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi · 2023
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Chatgpt and large language models in academia: opportunities and challenges
Jesse G Meyer, Ryan J Urbanowicz, Patrick CN Martin, Karen O’Connor, Ruowang Li, Pei-Chen Peng, Tiffani J Bright, Nicholas Tatonetti, Kyoung Jae Won, Graciela Gonzalez-Hernandez, et al · 2023
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Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
Cited alongside, same era.
On the opportunities and risks of foundation models for natural language processing in radiology
Walter F Wiggins and Ali S Tejani · 2022
Cited alongside, same era.
Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong · 2023
Cited alongside, same era.
Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
Cited alongside, same era.
More than you’ve asked for: A comprehensive analysis of novel prompt injection threats to application-integrated large language models
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz · 2023
Cited alongside, same era.
Large language models can be used to effectively scale spear phishing campaigns
Julian Hazell · 2023
Cited alongside, same era.
Automatically auditing large language models via discrete optimization
Erik Jones, Anca Dragan, Aditi Raghunathan, and Jacob Steinhardt · 2023
Cited alongside, same era.
Exploiting programmatic behavior of llms: Dual-use through standard security attacks
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, and Tatsunori Hashimoto · 2023
Cited alongside, same era.
Later among the works it cites.
Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Zephyr: Direct distillation of lm alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, et al · 2023
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Fundamental limitations of alignment in large language models
Yotam Wolf, Noam Wies, Oshri Avnery, Yoav Levine, and Amnon Shashua · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson · 2023
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Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A Choquette-Choo, Matthew Jagielski, Irena Gao, Pang Wei W Koh, Daphne Ippolito, Florian Tramer, and Ludwig Schmidt · 2024
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Harmbench: A standardized evaluation framework for automated red teaming and robust refusal
Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, et al · 2024
Closest in time.
Learning or self-aligning? rethinking instruction fine-tuning
Mengjie Ren, Boxi Cao, Hongyu Lin, Liu Cao, Xianpei Han, Ke Zeng, Guanglu Wan, Xunliang Cai, and Le Sun · 2024
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Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt · 2024
Closest in time.
Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2024
Closest in time.