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Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
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Prevent the language model from being overconfident in neural machine translation
Mengqi Miao, Fandong Meng, Yijin Liu, Xiao-Hua Zhou, and Jie Zhou. 2021 · 2021
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, and et.al. 2022 · 2022
Earlier work this paper cites.
Towards improving faithfulness in abstractive summarization
Xiuying Chen, Mingzhe Li, Xin Gao, and Xiangliang Zhang. 2022 · 2022
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
Introducing chatgpt
OpenAI. 2022 · 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 · 2022
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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 · 2022
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Introducing claude
Anthropic. 2023 · 2023
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. 2023 · 2023
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Improving translation faithfulness of large language models via augmenting instructions
Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, and Jie Zhou. 2023 · 2023
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Chat gpt "dan" (and other "jailbreaks")
DAN. 2023 · 2023
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Jailbreaker: Automated Jailbreak Across Multiple Large Language Model Chatbots
Gelei Deng, Yi Liu, Yuekang Li, Kailong Wang, Ying Zhang, Zefeng Li, Haoyu Wang, Tianwei Zhang, and Yang Liu. 2023 · 2023
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Baseline defenses for adversarial attacks against aligned language models
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein. 2023 · 2023
Earlier work this paper cites.
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 · 2023
Cited alongside, same era.
Automatically auditing large language models via discrete optimization
Erik Jones, Anca Dragan, Aditi Raghunathan, and Jacob Steinhardt. 2023 · 2023
Cited alongside, same era.
RLAIF: Scaling reinforcement learning from human feedback with AI feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, and Sushant Prakash. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023 · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Promptbench: Towards evaluating the robustness of large language models on adversarial prompts
Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Zhenqiang Gong, et al. 2023 · 2023
Later among the works it cites.
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson. 2023 · 2023
Later among the works it cites.
Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing. 2024 · 2024
Closest in time.
A wolf in sheep’s clothing: Generalized nested jailbreak prompts can fool large language models easily
Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, and Shujian Huang. 2024 · 2024
Closest in time.
Catastrophic jailbreak of open-source LLMs via exploiting generation
Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen. 2024 · 2024
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Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Cited alongside, same era.
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2023 · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Jailbreak and guard aligned language models with only few in-context demonstrations
Zeming Wei, Yifei Wang, and Yisen Wang. 2023 · 2023
Cited alongside, same era.
Fundamental limitations of alignment in large language models
Yotam Wolf, Noam Wies, Yoav Levine, and Amnon Shashua. 2023 · 2023
Cited alongside, same era.
Defending chatgpt against jailbreak attack via self-reminders
Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu. 2023 · 2023
Cited alongside, same era.
Low-resource languages jailbreak GPT-4
Zheng Xin Yong, Cristina Menghini, and Stephen Bach. 2023 · 2023
Cited alongside, same era.
Gptfuzzer: Red teaming large language models with auto-generated jailbreak prompts
Jiahao Yu, Xingwei Lin, and Xinyu Xing. 2023 · 2023
Cited alongside, same era.
Closest in time.
GUARD: Role-playing to generate natural-language jailbreakings to test guideline adherence of large language models
Haibo Jin, Ruoxi Chen, Andy Zhou, Yang Zhang, and Haohan Wang. 2024 · 2024
Closest in time.
Open sesame! universal black-box jailbreaking of large language models
Raz Lapid, Ron Langberg, and Moshe Sipper. 2024 · 2024
Closest in time.
Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024 · 2024
Closest in time.
Trusting your evidence: Hallucinate less with context-aware decoding
Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Wen-tau Yih. 2024 · 2024
Closest in time.
Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2024 · 2024
Closest in time.
Cognitive overload: Jailbreaking large language models with overloaded logical thinking
Nan Xu, Fei Wang, Ben Zhou, Bangzheng Li, Chaowei Xiao, and Muhao Chen. 2024a · 2024
Closest in time.
A comprehensive study of jailbreak attack versus defense for large language models
Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li, and Stjepan Picek. 2024b · 2024
Closest in time.
GPT-4 is too smart to be safe: Stealthy chat with LLMs via cipher
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen tse Huang, Pinjia He, Shuming Shi, and Zhaopeng Tu. 2024 · 2024
Closest in time.
AutoDAN: Interpretable gradient-based adversarial attacks on large language models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun. 2024 · 2024
Closest in time.