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Large language models (LLMs) have been widely adopted in applications such as automated content generation and even critical decision-making systems.
Rouge: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin · 2004
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An Alternative Softmax Operator for Reinforcement Learning
Kavosh Asadi and Michael L. Littman · 2017
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HotFlip: White-Box Adversarial Examples for Text Classification
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2017
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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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Gradient-based Adversarial Attacks against Text Transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, and Douwe Kiela · 2021
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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
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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
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Retrieval-Augmented Generation for Large Language Models: A Survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang · 2023
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Automatically Auditing Large Language Models via Discrete Optimization
Erik Jones, Anca Dragan, Aditi Raghunathan, and Jacob Steinhardt · 2023
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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 · 2023
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Biases in Large Language Models: Origins, Inventory, and Discussion
Roberto Navigli, Simone Conia, and Björn Ross · 2023
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The OIG Dataset
Huu Nguyen, Sameer Suri, Ken Tsui, Shahules786, and Christoph Schuhmann · 2023
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Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao · 2023
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Adversarial Attacks on LLMs
Lilian Weng · 2023
Cited alongside, same era.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang · 2023
Cited alongside, same era.
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
Jiahao Yu, Xingwei Lin, Zheng Yu, and Xinyu Xing · 2023
Cited alongside, same era.
Universal and Transferable Adversarial Attacks on Aligned Language Models
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico Kolter, and Matt Fredrikson · 2023
Cited alongside, same era.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
Jailbreaking LLM-Controlled Robots
Alexander Robey, Zachary Ravichandran, Vijay Kumar, Hamed Hassani, and George J. Pappas · 2024
Closest in time.
Mathematical discoveries from program search with large language models
Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al · 2024
Closest in time.
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu, Eric Hambro, Aram H Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, et al · 2024
Closest in time.
The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts
Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, and Daniel Khashabi · 2024
Closest in time.
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Cited alongside, same era.
Imprompter: Tricking LLM Agents into Improper Tool Use
Xiaohan Fu, Shuheng Li, Zihan Wang, Yihao Liu, Rajesh K Gupta, Taylor Berg-Kirkpatrick, and Earlence Fernandes · 2024
Cited alongside, same era.
Coercing LLMs to do and reveal (almost) anything
Jonas Geiping, Alex Stein, Manli Shu, Khalid Saifullah, Yuxin Wen, and Tom Goldstein · 2024
Cited alongside, same era.
Query-based Adversarial Prompt Generation
Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, and Milad Nasr · 2024
Cited alongside, same era.
PLeak: Prompt Leaking Attacks against Large Language Model Applications
Bo Hui, Haolin Yuan, Neil Gong, Philippe Burlina, and Yinzhi Cao · 2024
Cited alongside, same era.
A Survey on Large Language Models for Code Generation
Juyong Jiang, Fan Wang, Jiasi Shen, Sungju Kim, and Sunghun Kim · 2024
Cited alongside, same era.
Making a SOTA Adversarial Attack on LLMs 38x Faster
Haize Labs · 2024
Cited alongside, same era.
AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs
Xiaogeng Liu, Peiran Li, Edward Suh, Yevgeniy Vorobeychik, Zhuoqing Mao, Somesh Jha, Patrick McDaniel, Huan Sun, Bo Li, and Chaowei Xiao · 2024
Cited alongside, same era.
Connor Shorten, Charles Pierse, Thomas Benjamin Smith, Erika Cardenas, Akanksha Sharma, John Trengrove, and Bob van Luijt · 2024
Closest in time.
Takeaways from the NeurIPS 2023 Trojan Detection Competition
Zygimantas Straznickas, T. Ben Thompson, and Michael Sklar · 2024
Closest in time.
RoleBreak: Character Hallucination as a Jailbreak Attack in Role-Playing Systems
Yihong Tang, Bo Wang, Xu Wang, Dongming Zhao, Jing Liu, Jijun Zhang, Ruifang He, and Yuexian Hou · 2024
Closest in time.
Solving olympiad geometry without human demonstrations
Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong · 2024
Closest in time.
An LLM can Fool Itself: A Prompt-Based Adversarial Attack
Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, and Mohan Kankanhalli · 2024
Closest in time.
An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, et al · 2024
Closest in time.
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models
Qingcheng Zeng, Mingyu Jin, Qinkai Yu, Zhenting Wang, Wenyue Hua, Zihao Zhou, Guangyan Sun, Yanda Meng, Shiqing Ma, Qifan Wang, et al · 2024
Closest in time.
Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi · 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
Closest in time.