Fetching the paper…
Reading the bibliography…
The rapid advancement of Large Language Models (LLMs) has brought about remarkable generative capabilities but also raised concerns about their potential misuse.
Build it break it fix it for dialogue safety: Robustness from adversarial human attack
Emily Dinan, Samuel Humeau, Bharath Chintagunta, and Jason Weston. 2019 · 1908
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Christiano, Paul F, Leike, Jan, Brown, Tom, Martic, Miljan, Legg, Shane, Amodei, and Dario. 2017 · 2017
Earlier work this paper cites.
Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
Wallace Eric, Rodriguez Pedro, Feng Shi, Yamada Ikuya, and Boyd-Graber Jordan. 2019 · 2019
Earlier work this paper cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel. 2021 · 2021
Earlier work this paper cites.
Gradient-based adversarial attacks against text transformers
Guo Chuan, Sablayrolles Alexandre, Jégou Hervé, and Kiela Douwe. 2021 · 2021
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, and et al. 2022 · 2022
Earlier work this paper cites.
The stack: 3 tb of permissively licensed source code
Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, and Harm de Vries. 2022 · 2022
Earlier work this paper cites.
Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 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, and et al. 2022 · 2022
Earlier work this paper cites.
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
Earlier work this paper cites.
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022 · 2022
Earlier work this paper cites.
Model card and evaluations for claude models
Anthropic. 2023 · 2023
Earlier work this paper cites.
Emergent autonomous scientific research capabilities of large language models
Daniil A. Boiko, Robert MacKnight, and Gabe Gomes. 2023 · 2023
Earlier work this paper cites.
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
Earlier work this paper cites.
Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing. 2023 · 2023
Cited alongside, same era.
Annollm: Making large language models to be better crowdsourced annotators
Xingwei He, Zhenghao Lin, Yeyun Gong, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, Weizhu Chen, and et al. 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.
Automatically auditing large language models via discrete optimization
Erik Jones, Anca Dragan, Aditi Raghunathan, and Jacob Steinhardt. 2023 · 2023
Cited alongside, same era.
Exploiting programmatic behavior of llms: Dual-use through standard security attacks
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, and et al. 2023 · 2023
Later among the works it cites.
Jailbroken: How does LLM safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 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.
Generating stealthy jailbreak prompts on aligned large language models
Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao. 2024 · 2024
Closest in time.
OpenAI. 2024 · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei A. Zaharia, and Tatsunori Hashimoto. 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.
Flirt: Feedback loop in-context red teaming
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, and Rahul Gupta. 2023 · 2023
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023 · 2023
Cited alongside, same era.
Codegeex: A pre-trained model for code generation with multilingual benchmarking on humaneval-x
Zheng Qinkai, Xia Xiao, Zou Xu, Dong Yuxiao, Wang Shan, Xue Yufei, Shen Lei, Wang Zihan, Wang Andi, Li Yang, Su Teng, Yang Zhilin, and Tang Jie. 2023 · 2023
Cited alongside, same era.
Smoothllm: Defending large language models against jailbreaking attacks
Alexander Robey, Eric Wong, Hamed Hassani, and George J Pappas. 2023 · 2023
Cited alongside, same era.
Scalable and transferable black-box jailbreaks for language models via persona modulation
Rusheb Shah, Quentin Feuillade-Montixi, Soroush Pour, Arush Tagade, Stephen Casper, and Javier Rando. 2023 · 2023
Cited alongside, same era.
Principle-driven self-alignment of language models from scratch with minimal human supervision
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, , and Chuang Gan. 2023 · 2023
Cited alongside, same era.
Closest in time.
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. 2024 · 2024
Closest in time.
Towards tracing trustworthiness dynamics: Revisiting pre-training period of large language models
Chen Qian, Jie Zhang, Wei Yao, Dongrui Liu, Zhenfei Yin, Yu Qiao, Yong Liu, and Jing Shao. 2024 · 2024
Closest in time.
Identifying semantic induction heads to understand in-context learning
Jie Ren, Qipeng Guo, Hang Yan, Dongrui Liu, Xipeng Qiu, and Dahua Lin. 2024 · 2024
Closest in time.
Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2024 · 2024
Closest in time.
Jailbreak and guard aligned language models with only few in-context demonstrations
Zeming Wei, Yifei Wang, Ang Li, Yichuan Mo, and Yisen Wang. 2024 · 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.
Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi. 2024 · 2024
Closest in time.
Psysafe: A comprehensive framework for psychological-based attack, defense, and evaluation of multi-agent system safety
Zaibin Zhang, Yongting Zhang, Lijun Li, Hongzhi Gao, Lijun Wang, Huchuan Lu, Feng Zhao, Yu Qiao, and Jing Shao. 2024 · 2024
Closest in time.