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The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons.
Aligning ai with shared human values
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt · 2008
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2009
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Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains
Eric M. Hutchins, Michael J. Cloppert, and Rohan M. Amin · 2011
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Council regulation (EU) no 269/2014, 2014
Council of European Union · 2014
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Pointer sentinel mixture models, 2016
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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California consumer privacy act, 2018
CCPA · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Inoculating science against potential pandemics and information hazards
Kevin M Esvelt · 2018
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Information hazards in biotechnology
Gregory Lewis, Piers Millett, Anders Sandberg, Andrew Snyder-Beattie, and Gigi Gronvall · 2019
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Universal adversarial triggers for attacking and analyzing nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
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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
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Forgetting outside the box: Scrubbing deep networks of information accessible from input-output observations
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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Learn to forget: Machine unlearning via neuron masking
Yang Liu, Zhuo Ma, Ximeng Liu, Jian Liu, Zhongyuan Jiang, Jianfeng Ma, Philip Yu, and Kui Ren · 2020
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Mitre att&ck: Design and philosophy
Blake E. Strom, Andy Applebaum, Doug P. Miller, Kathryn C. Nickels, Adam G. Pennington, and Cody B. Thomas · 2020
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Fine-tuning language models from human preferences, 2020
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2020
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A framework for few-shot language model evaluation, September 2021
Leo Gao, Jonathan Tow, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Kyle McDonell, Niklas Muennighoff, Jason Phang, Laria Reynolds, Eric Tang, Anish Thite, Ben Wang, Kevin Wang, and Andy Zou · 2021
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Gradient-based adversarial attacks against text transformers, 2021
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, and Douwe Kiela · 2021
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Unsolved problems in ml safety
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2021
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Mitigating harm in language models with conditional-likelihood filtration
Helen Ngo, Cooper D. Raterink, Joao M. de Ara’ujo, Ivan Zhang, Carol Chen, Adrien Morisot, and Nick Frosst · 2021
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Constitutional ai: Harmlessness from ai feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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Discovering latent knowledge in language models without supervision, 2022
Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt · 2022
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Delay, Detect, Defend: Preparing for a Future in which Thousands Can Release New Pandemics
Kevin M. Esvelt · 2022
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The emerging threat of ai-driven cyber attacks: A review
Blessing Guembe, Ambrose Azeta, Sanjay Misra, Victor Chukwudi Osamor, Luis Fernandez-Sanz, and Vera Pospelova · 2022
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X-risk analysis for ai research, 2022
Dan Hendrycks and Mantas Mazeika · 2022
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Locating and editing factual associations in GPT
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 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
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Structured access: an emerging paradigm for safe ai deployment, 2022
Toby Shevlane · 2022
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Dual use of artificial-intelligence-powered drug discovery
Fabio Urbina, Filippa Lentzos, Cédric Invernizzi, and Sean Ekins · 2022
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Interpretability in the wild: a circuit for indirect object identification in gpt-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2022
Cited alongside, same era.
GitHub - 01-ai/Yi: A series of large language models trained from scratch by developers @01-ai — github.com
01-ai · 2023
Cited alongside, same era.
Anthropic’s Responsible Scaling Policy — anthropic.com
Anthropic · 2023
Cited alongside, same era.
Leace: Perfect linear concept erasure in closed form
Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, and Stella Biderman · 2023
Cited alongside, same era.
Purple llama cyberseceval: A secure coding benchmark for language models, 2023
Manish Bhatt, Sahana Chennabasappa, Cyrus Nikolaidis, Shengye Wan, Ivan Evtimov, Dominik Gabi, Daniel Song, Faizan Ahmad, Cornelius Aschermann, Lorenzo Fontana, Sasha Frolov, Ravi Prakash Giri, Dhaval Kapil, Yiannis Kozyrakis, David LeBlanc, James Milazzo, Aleksandar Straumann, Gabriel Synnaeve, Varun Vontimitta, Spencer Whitman, and Joshua Saxe · 2023
Gpqa: A graduate-level google-proof q&a benchmark
David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman · 2023
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Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools, 2023
Jonas B. Sandbrink · 2023
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Jérémy Scheurer, Mikita Balesni, and Marius Hobbhahn · 2023
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Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting, 2023
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2023
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Cited alongside, same era.
Autonomous chemical research with large language models
Daniil A Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes · 2023
Cited alongside, same era.
Jailbreaking black box large language models in twenty queries, 2023
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong · 2023
Cited alongside, same era.
Towards machine unlearning benchmarks: Forgetting the personal identities in facial recognition systems, 2023
Dasol Choi and Dongbin Na · 2023
Cited alongside, same era.
Toxicity in chatgpt: Analyzing persona-assigned language models
Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan · 2023
Cited alongside, same era.
Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich · 2023
Cited alongside, same era.
Towards adversarial evaluations for inexact machine unlearning, 2023
Shashwat Goel, Ameya Prabhu, Amartya Sanyal, Ser-Nam Lim, Philip Torr, and Ponnurangam Kumaraguru · 2023
Cited alongside, same era.
Neurips 2023 machine unlearning challenge, 2023
Google · 2023
Cited alongside, same era.
Toby Shevlane, Sebastian Farquhar, Ben Garfinkel, Mary Phuong, Jess Whittlestone, Jade Leung, Daniel Kokotajlo, Nahema Marchal, Markus Anderljung, Noam Kolt, et al · 2023
Later among the works it cites.
Zephyr: Direct distillation of lm alignment, 2023
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
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Activation addition: Steering language models without optimization, 2023
Alexander Matt Turner, Lisa Thiergart, David Udell, Gavin Leech, Ulisse Mini, and Monte MacDiarmid · 2023
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The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023 — gov.uk
UK AI Safety Summit · 2023
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National risk register
UK Cabinet Office · 2023
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Jailbroken: How does llm safety training fail?
Alexander Wei, Nika Haghtalab, and Jacob Steinhardt · 2023
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Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
The White House · 2023
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Shadow alignment: The ease of subverting safely-aligned language models, 2023
Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, and Dahua Lin · 2023
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Gpt-4 is too smart to be safe: Stealthy chat with llms via cipher, 2023
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen tse Huang, Pinjia He, Shuming Shi, and Zhaopeng Tu · 2023
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Removing rlhf protections in gpt-4 via fine-tuning, 2023
Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, and Daniel Kang · 2023
Later among the works it cites.
Siren’s song in the ai ocean: A survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Later among the works it cites.
Export administration regulations (ear), 15 cfr parts 730-774
EAR · 2024
Closest in time.
Llm agents can autonomously hack websites, 2024
Richard Fang, Rohan Bindu, Akul Gupta, Qiusi Zhan, and Daniel Kang · 2024
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Global cybersecurity outlook 2024, 2024
World Economic Forum · 2024
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Fast machine unlearning without retraining through selective synaptic dampening
Jack Foster, Stefan Schoepf, and Alexandra Brintrup · 2024
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Corrective machine unlearning, 2024
Shashwat Goel, Ameya Prabhu, Philip Torr, Ponnurangam Kumaraguru, and Amartya Sanyal · 2024
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International traffic in arms regulations (itar), 22 cfr parts 120-130
ITAR · 2024
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Mixtral of experts, 2024
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2024
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Towards Safer Large Language Models through Machine Unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, and Meng Jiang · 2024
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Eight methods to evaluate robust unlearning in llms, 2024
Aengus Lynch, Phillip Guo, Aidan Ewart, Stephen Casper, and Dylan Hadfield-Menell · 2024
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Tofu: A task of fictitious unlearning for llms, 2024
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, and J. Zico Kolter · 2024
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Harmbench: A standardized evaluation framework for automated red teaming and robust refusal
Mantas Mazeika, Long Phan, Xuwang Yin, Andy Zou, Zifan Wang, Norman Mu, Elham Sakhaee, Nathaniel Li, Steven Basart, Bo Li, et al · 2024
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The Operational Risks of AI in Large-Scale Biological Attacks: Results of a Red-Team Study
Christopher A. Mouton, Caleb Lucas, and Ella Guest · 2024
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Building an early warning system for LLM-aided biological threat creation — openai.com
OpenAI · 2024
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Feedback loops with language models drive in-context reward hacking
Alexander Pan, Erik Jones, Meena Jagadeesan, and Jacob Steinhardt · 2024
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Evaluating frontier models for dangerous capabilities, 2024
Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan, Alexandre Kaskasoli, Victoria Krakovna, David Lindner, Matthew Rahtz, Yannis Assael, Sarah Hodkinson, Heidi Howard, Tom Lieberum, Ramana Kumar, Maria Abi Raad, Albert Webson, Lewis Ho, Sharon Lin, Sebastian Farquhar, Marcus Hutter, Gregoire Deletang, Anian Ruoss, Seliem El-Sayed, Sasha Brown, Anca Dragan, Rohin Shah, Allan Dafoe, and Toby Shevlane · 2024
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Soft prompt threats: Attacking safety alignment and unlearning in open-source llms through the embedding space, 2024
Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, and Stephan Gunnemann · 2024
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