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Large Language Models (LLMs) have emerged as powerful tools, but their inherent safety risks - ranging from harmful content generation to broader societal harms - pose significant challenges.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2009
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
interpreting gpt: the logit lens
Nostalgebraist. 2020 · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. 2021 · 2021
Earlier work this paper cites.
A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, et al. 2021 · 2021
Earlier work this paper cites.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 2021 · 2021
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Winogrande: An adversarial winograd schema challenge at scale
Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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Fudge: Controlled text generation with future discriminators
Kevin Yang and Dan Klein. 2021 · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022
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Why should adversarial perturbations be imperceptible? rethink the research paradigm in adversarial NLP
Yangyi Chen, Hongcheng Gao, Ganqu Cui, Fanchao Qi, Longtao Huang, Zhiyuan Liu, and Maosong Sun. 2022 · 2022
Earlier work this paper cites.
Predictability and surprise in large generative models
Deep Ganguli, Danny Hernandez, Liane Lovitt, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova Dassarma, Dawn Drain, Nelson Elhage, et al. 2022 · 2022
Earlier work this paper cites.
Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. 2022 · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al. 2022 · 2022
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Anthropic’s responsible scaling policy,
Anthropic. Sep 19, 2023 · 2023
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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023 · 2023
Earlier work this paper cites.
Eliciting latent predictions from transformers with the tuned lens
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. 2023 · 2023
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On hate scaling laws for data-swamps
Abeba Birhane, Vinay Prabhu, Sang Han, and Vishnu Naresh Boddeti. 2023 · 2023
Earlier work this paper cites.
Harms from increasingly agentic algorithmic systems
Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, et al. 2023 · 2023
Earlier work this paper cites.
Analyzing transformers in embedding space
Guy Dar, Mor Geva, Ankit Gupta, and Jonathan Berant. 2023 · 2023
Earlier work this paper cites.
Enhancing chat language models by scaling high-quality instructional conversations
Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. 2023 · 2023
Earlier work this paper cites.
Who’s harry potter? approximate unlearning in llms
Ronen Eldan and Mark Russinovich. 2023 · 2023
Earlier work this paper cites.
Language models represent space and time
Wes Gurnee and Max Tegmark. 2023 · 2023
Earlier work this paper cites.
An overview of catastrophic ai risks
Dan Hendrycks, Mantas Mazeika, and Thomas Woodside. 2023 · 2023
Earlier work this paper cites.
Knowledge unlearning for mitigating privacy risks in language models
Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, and Minjoon Seo. 2023 · 2023
Earlier work this paper cites.
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.
Improving activation steering in language models with mean-centring
Ole Jorgensen, Dylan Cope, Nandi Schoots, and Murray Shanahan. 2023 · 2023
Cited alongside, same era.
Lora fine-tuning efficiently undoes safety training in llama 2-chat 70b
Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish. 2023 · 2023
Cited alongside, same era.
The unlocking spell on base llms: Rethinking alignment via in-context learning
Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu, Nouha Dziri, Melanie Sclar, Khyathi Chandu, Chandra Bhagavatula, and Yejin Choi. 2023 · 2023
Cited alongside, same era.
Keeping LLMs aligned after fine-tuning: The crucial role of prompt templates
Kaifeng Lyu, Haoyu Zhao, Xinran Gu, Dingli Yu, Anirudh Goyal, and Sanjeev Arora. 2024 · 2024
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Tofu: A task of fictitious unlearning for llms
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C Lipton, and J Zico Kolter. 2024 · 2024
Later among the works it cites.
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 · 2024
Later among the works it cites.
Gemma: Open models based on gemini research and technology
Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. 2024 · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Sheng Liu, Haotian Ye, Lei Xing, and James Zou. 2023 · 2023
Cited alongside, same era.
Steering llama 2 via contrastive activation addition
Nina Panickssery, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, and Alexander Matt Turner. 2023 · 2023
Cited alongside, same era.
Kellin Pelrine, Mohammad Taufeeque, Michał Zając, Euan McLean, and Adam Gleave. 2023 · 2023
Cited alongside, same era.
Xstest: A test suite for identifying exaggerated safety behaviours in large language models
Paul Röttger, Hannah Rose Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. 2023 · 2023
Cited alongside, same era.
Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang. 2023 · 2023
Cited alongside, same era.
Bypassing the safety training of open-source llms with priming attacks
Jason Vega, Isha Chaudhary, Changming Xu, and Gagandeep Singh. 2023 · 2023
Cited alongside, same era.
Large language model unlearning
Yuanshun Yao, Xiaojun Xu, and Yang Liu. 2023 · 2023
Cited alongside, same era.
Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 2023 · 2023
Cited alongside, same era.
Richard Ngo, Lawrence Chan, and Sören Mindermann. 2024 · 2024
Later among the works it cites.
Navigating the safety landscape: Measuring risks in finetuning large language models
ShengYun Peng, Pin-Yu Chen, Matthew Hull, and Duen Horng Chau. 2024 · 2024
Later among the works it cites.
Evaluating frontier models for dangerous capabilities
Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan, Alexandre Kaskasoli, Victoria Krakovna, David Lindner, Matthew Rahtz, Yannis Assael, Sarah Hodkinson, et al. 2024 · 2024
Later among the works it cites.
Spectral editing of activations for large language model alignment
Yifu Qiu, Zheng Zhao, Yftah Ziser, Anna Korhonen, Edoardo Ponti, and Shay B Cohen. 2024 · 2024
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Representation noising: A defence mechanism against harmful finetuning
Domenic Rosati, Jan Wehner, Kai Williams, Lukasz Bartoszcze, Robie Gonzales, carsten maple, Subhabrata Majumdar, Hassan Sajjad, and Frank Rudzicz. 2024 · 2024
Later among the works it cites.
Leo Schwinn, David Dobre, Sophie Xhonneux, Gauthier Gidel, and Stephan Gunnemann. 2024 · 2024
Later among the works it cites.
Muse: Machine unlearning six-way evaluation for language models
Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah A Smith, and Chiyuan Zhang. 2024 · 2024
Later among the works it cites.
Ununlearning: Unlearning is not sufficient for content regulation in advanced generative ai
Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jimenez, Nicolas Papernot, Matthew Jagielski, Itay Yona, Heidi Howard, and Eugene Bagdasaryan. 2024 · 2024
Later among the works it cites.
Analysing the generalisation and reliability of steering vectors
Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adrià Garriga-Alonso, and Robert Kirk. 2024 · 2024
Later among the works it cites.
Guardrail baselines for unlearning in llms
Pratiksha Thaker, Yash Maurya, Shengyuan Hu, Zhiwei Steven Wu, and Virginia Smith. 2024 · 2024
Later among the works it cites.
Connecting the dots: Llms can infer and verbalize latent structure from disparate training data
Johannes Treutlein, Dami Choi, Jan Betley, Samuel Marks, Cem Anil, Roger B Grosse, and Owain Evans. 2024 · 2024
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Steering language models with activation engineering
Alexander Matt Turner, Lisa Thiergart, Gavin Leech, David Udell, Juan J. Vazquez, Ulisse Mini, and Monte MacDiarmid. 2024 · 2024
Later among the works it cites.
A language model’s guide through latent space
Dimitri von Rütte, Sotiris Anagnostidis, Gregor Bachmann, and Thomas Hofmann. 2024 · 2024
Later among the works it cites.
Yu Wang, Ruihan Wu, Zexue He, Xiusi Chen, and Julian McAuley. 2024 · 2024
Later among the works it cites.
Reft: Representation finetuning for language models
Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D Manning, and Christopher Potts. 2024 · 2024
Later among the works it cites.
Uncovering safety risks of large language models through concept activation vector
Zhihao Xu, Ruixuan Huang, Changyu Chen, and Xiting Wang. 2024 · 2024
Later among the works it cites.
Robust llm safeguarding via refusal feature adversarial training
Lei Yu, Virginie Do, Karen Hambardzumyan, and Nicola Cancedda. 2024 · 2024
Later among the works it cites.
How johnny can persuade LLMs to jailbreak them: Rethinking persuasion to challenge AI safety by humanizing LLMs
Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi. 2024 · 2024
Later among the works it cites.
Removing RLHF protections in GPT-4 via fine-tuning
Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, and Daniel Kang. 2024 · 2024
Later among the works it cites.
Negative preference optimization: From catastrophic collapse to effective unlearning
Ruiqi Zhang, Licong Lin, Yu Bai, and Song Mei. 2024 · 2024
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Improving alignment and robustness with circuit breakers
Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, J Zico Kolter, Matt Fredrikson, and Dan Hendrycks. 2024 · 2024
Later among the works it cites.
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico Kolter, and Matt Fredrikson. 2023b · 2024
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Guidedbench: Equipping jailbreak evaluation with guidelines
Ruixuan Huang, Xunguang Wang, Zongjie Li, Daoyuan Wu, and Shuai Wang. 2025 · 2025
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