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The recent surge in jailbreaking attacks has revealed significant vulnerabilities in Large Language Models (LLMs) when exposed to malicious inputs.
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 · 1909
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Sparse coding with an overcomplete basis set: A strategy employed by v1?
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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 · 2009
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Sparse and redundant representations: from theory to applications in signal and image processing
Michael Elad. 2010 · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
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What does bert learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 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 · 2019
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Two views on the cognitive brain
David L Barack and John W Krakauer. 2021 · 2021
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Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al. 2021 · 2021
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Discovering latent knowledge in language models without supervision
Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Nelson Elhage, Tristan Hume, Catherine Olsson, Nicholas Schiefer, Tom Henighan, Shauna Kravec, Zac Hatfield-Dodds, Robert Lasenby, Dawn Drain, Carol Chen, et al. 2022 · 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 · 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 · 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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Polysemanticity and capacity in neural networks
Adam Scherlis, Kshitij Sachan, Adam S Jermyn, Joe Benton, and Buck Shlegeris. 2022 · 2022
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On second thought, let’s not think step by step! bias and toxicity in zero-shot reasoning
Omar Shaikh, Hongxin Zhang, William Held, Michael Bernstein, and Diyi Yang. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Falcon-40B: an open large language model with state-of-the-art performance
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023 · 2023
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The internal state of an llm knows when its lying
Amos Azaria and Tom Mitchell. 2023 · 2023
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 · 2023
Cited alongside, same era.
MASTERKEY: Automated jailbreaking of 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
Cited alongside, same era.
Peng Ding, Jun Kuang, Dan Ma, Xuezhi Cao, Yunsen Xian, Jiajun Chen, and Shujian Huang. 2023 · 2023
Cited alongside, same era.
Towards explainable and language-agnostic llms: symbolic reverse engineering of language at scale
Walid S Saba. 2023 · 2023
Later among the works it cites.
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2023 · 2023
Later among the works it cites.
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
Varshini Subhash, Anna Bialas, Weiwei Pan, and Finale Doshi-Velez. 2023 · 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 · 2023
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Josh A Goldstein, Girish Sastry, Micah Musser, Renee DiResta, Matthew Gentzel, and Katerina Sedova. 2023 · 2023
Cited alongside, same era.
Inspecting and editing knowledge representations in language models
Evan Hernandez, Belinda Z Li, and Jacob Andreas. 2023 · 2023
Cited alongside, same era.
Token-level adversarial prompt detection based on perplexity measures and contextual information
Zhengmian Hu, Gang Wu, Saayan Mitra, Ruiyi Zhang, Tong Sun, Heng Huang, and Vishy Swaminathan. 2023 · 2023
Cited alongside, same era.
C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models
Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, and Junxian He. 2023 · 2023
Cited alongside, same era.
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
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 · 2023
Cited alongside, same era.
Pretraining language models with human preferences
Tomasz Korbak, Kejian Shi, Angelica Chen, Rasika Vinayak Bhalerao, Christopher Buckley, Jason Phang, Samuel R Bowman, and Ethan Perez. 2023 · 2023
Cited alongside, same era.
Tree of attacks: Jailbreaking black-box llms automatically
Anay Mehrotra, Manolis Zampetakis, Paul Kassianik, Blaine Nelson, Hyrum Anderson, Yaron Singer, and Amin Karbasi. 2023 · 2023
Cited alongside, same era.
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2023 · 2023
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Activation addition: Steering language models without optimization
Alex Turner, Lisa Thiergart, David Udell, Gavin Leech, Ulisse Mini, and Monte MacDiarmid. 2023 · 2023
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Label words are anchors: An information flow perspective for understanding in-context learning
Lean Wang, Lei Li, Damai Dai, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, and Xu Sun. 2023 · 2023
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Cognitive overload: Jailbreaking large language models with overloaded logical thinking
Nan Xu, Fei Wang, Ben Zhou, Bang Zheng Li, Chaowei Xiao, and Muhao Chen. 2023 · 2023
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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. 2023 · 2023
Later among the works it cites.
Defending large language models against jailbreaking attacks through goal prioritization
Zhexin Zhang, Junxiao Yang, Pei Ke, and Minlie Huang. 2023 · 2023
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Autodan: Automatic and interpretable 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. 2023 · 2023
Later among the works it cites.
Yi: Open foundation models by 01.ai
01. AI, :, Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, Kaidong Yu, Peng Liu, Qiang Liu, Shawn Yue, Senbin Yang, Shiming Yang, Tao Yu, Wen Xie, Wenhao Huang, Xiaohui Hu, Xiaoyi Ren, Xinyao Niu, Pengcheng Nie, Yuchi Xu, Yudong Liu, Yue Wang, Yuxuan Cai, Zhenyu Gu, Zhiyuan Liu, and Zonghong Dai. 2024 · 2024
Closest in time.
Llama 3 model card
AI@Meta. 2024 · 2024
Closest in time.
Inside: Llms’ internal states retain the power of hallucination detection
Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, Mingyuan Tao, Zhihang Fu, and Jieping Ye. 2024 · 2024
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Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2024 · 2024
Closest in time.
Sorry-bench: Systematically evaluating large language model safety refusal behaviors
Tinghao Xie, Xiangyu Qi, Yi Zeng, Yangsibo Huang, Udari Madhushani Sehwag, Kaixuan Huang, Luxi He, Boyi Wei, Dacheng Li, Ying Sheng, Ruoxi Jia, Bo Li, Kai Li, Danqi Chen, Peter Henderson, and Prateek Mittal. 2024 · 2024
Closest in time.
Weak-to-strong jailbreaking on large language models
Xuandong Zhao, Xianjun Yang, Tianyu Pang, Chao Du, Lei Li, Yu-Xiang Wang, and William Yang Wang. 2024 · 2024
Closest in time.
Llamafactory: Unified efficient fine-tuning of 100+ language models
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. 2024 · 2024
Closest in time.