Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Models

Visualizing data using t-sne

Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008

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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned

Original

Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, and Jack Clark. 2022 · 2022

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Gpt-4 technical report

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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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Qwen technical report

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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu. 2023 · 2023

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Jailbreaking black box large language models in twenty queries

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Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J Pappas, and Eric Wong. 2023 · 2023

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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, Ion Stoica, and Eric P. Xing. 2023 · 2023

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Qwen-audio: Advancing universal audio understanding via unified large-scale audio-language models

Original

Yunfei Chu, Jin Xu, Xiaohuan Zhou, Qian Yang, Shiliang Zhang, Zhijie Yan, Chang Zhou, and Jingren Zhou. 2023 · 2023

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Figstep: Jailbreaking large vision-language models via typographic visual prompts

Original

Yichen Gong, Delong Ran, Jinyuan Liu, Conglei Wang, Tianshuo Cong, Anyu Wang, Sisi Duan, and Xiaoyun Wang. 2023 · 2023

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Walking a tightrope - evaluating large language models in high-risk domains

Original

Chia-Chien Hung, Wiem Ben Rim, Lindsay Frost, Lars Brückner, and Carolin Lawrence. 2023 · 2023

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Llama guard: Llm-based input-output safeguard for human-ai conversations

Original

Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa. 2023 · 2023

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Deepinception: Hypnotize large language model to be jailbreaker

Original

Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, and Bo Han. 2023 · 2023

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Can llms follow simple rules?

Original

Norman Mu, Sarah Chen, Zifan Wang, Sizhe Chen, David Karamardian, Lulwa Aljeraisy, Dan Hendrycks, and David A. Wagner. 2023 · 2023

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Llama: Open and efficient foundation language models

Original

Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023

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Do-not-answer: A dataset for evaluating safeguards in llms

Original

Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, and Timothy Baldwin. 2023 · 2023

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Cvalues: Measuring the values of chinese large language models from safety to responsibility

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Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, Ji Zhang, Chao Peng, Fei Huang, and Jingren Zhou. 2023 · 2023

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Investigating Pre-trained Audio Encoders in the Low-Resource Condition

Hao Yang, Jinming Zhao, Gholamreza Haffari, and Ehsan Shareghi. 2023 · 2023

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Safetybench: Evaluating the safety of large language models with multiple choice questions

Original

Zhexin Zhang, Leqi Lei, Lindong Wu, Rui Sun, Yongkang Huang, Chong Long, Xiao Liu, Xuanyu Lei, Jie Tang, and Minlie Huang. 2023 · 2023

Cited alongside, same era.

Safetyprompts: a systematic review of open datasets for evaluating and improving large language model safety

Original

Paul Röttger, Fabio Pernisi, Bertie Vidgen, and Dirk Hovy. 2024 · 2024

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Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models

Erfan Shayegani, Yue Dong, and Nael B. Abu-Ghazaleh. 2024 · 2024

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SALMONN: towards generic hearing abilities for large language models

Changli Tang, Wenyi Yu, Guangzhi Sun, Xianzhao Chen, Tian Tan, Wei Li, Lu Lu, Zejun Ma, and Chao Zhang. 2024 · 2024

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ALERT: A comprehensive benchmark for assessing large language models’ safety through red teaming

Original

Simone Tedeschi, Felix Friedrich, Patrick Schramowski, Kristian Kersting, Roberto Navigli, Huu Nguyen, and Bo Li. 2024 · 2024

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SELF-GUARD: empower the LLM to safeguard itself

Zezhong Wang, Fangkai Yang, Lu Wang, Pu Zhao, Hongru Wang, Liang Chen, Qingwei Lin, and Kam-Fai Wong. 2024b · 2024