Fetching the paper…
Reading the bibliography…
Harmful fine-tuning attack poses serious safety concerns for large language models' fine-tuning-as-a-service.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Fixing weight decay regularization in adam
Ilya Loshchilov, Frank Hutter, et al · 2017
Earlier work this paper cites.
Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 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
Earlier work this paper cites.
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
Earlier work this paper cites.
Achieving personalized federated learning with sparse local models
Tiansheng Huang, Shiwei Liu, Li Shen, Fengxiang He, Weiwei Lin, and Dacheng Tao · 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, et al · 2022
Earlier work this paper cites.
Boosting the transferability of adversarial attacks with reverse adversarial perturbation
Zeyu Qin, Yanbo Fan, Yi Liu, Li Shen, Yong Zhang, Jue Wang, and Baoyuan Wu · 2022
Earlier work this paper cites.
Penalizing gradient norm for efficiently improving generalization in deep learning
Yang Zhao, Hao Zhang, and Xiuyuan Hu · 2022
Earlier work this paper cites.
Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, Paul Röttger, Dan Jurafsky, Tatsunori Hashimoto, and James Zou · 2023
Earlier work this paper cites.
Safe rlhf: Safe reinforcement learning from human feedback
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang · 2023
Earlier work this paper cites.
Raft: Reward ranked finetuning for generative foundation model alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang · 2023
Earlier work this paper cites.
Large language model-powered smart contract vulnerability detection: New perspectives
Sihao Hu, Tiansheng Huang, Fatih İlhan, Selim Furkan Tekin, and Ling Liu · 2023
Earlier work this paper cites.
Fusion of global and local knowledge for personalized federated learning
Tiansheng Huang, Li Shen, Yan Sun, Weiwei Lin, and Dacheng Tao · 2023
Earlier work this paper cites.
Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Juntao Dai, Xuehai Pan, Chi Zhang, Ce Bian, Ruiyang Sun, Yizhou Wang, and Yaodong Yang · 2023
Earlier work this paper cites.
Lora fine-tuning efficiently undoes safety training in llama 2-chat 70b
Simon Lermen, Charlie Rogers-Smith, and Jeffrey Ladish · 2023
Earlier work this paper cites.
Training socially aligned language models in simulated human society
Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M Dai, Diyi Yang, and Soroush Vosoughi · 2023
Earlier work this paper cites.
Fine-tuning can cripple your foundation model; preserving features may be the solution
Jishnu Mukhoti, Yarin Gal, Philip HS Torr, and Puneet K Dokania · 2023
Earlier work this paper cites.
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 · 2023
Earlier work this paper cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn · 2023
Earlier work this paper cites.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Earlier work this paper cites.
Pairwise proximal policy optimization: Harnessing relative feedback for llm alignment
Tianhao Wu, Banghua Zhu, Ruoyu Zhang, Zhaojin Wen, Kannan Ramchandran, and Jiantao Jiao · 2023
Earlier work this paper cites.
Shadow alignment: The ease of subverting safely-aligned language models
Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, and Dahua Lin · 2023
Cited alongside, same era.
Selfee: Iterative self-revising llm empowered by self-feedback generation
Seonghyeon Ye, Yongrae Jo, Doyoung Kim, Sungdong Kim, Hyeonbin Hwang, and Minjoon Seo · 2023
Cited alongside, same era.
Rrhf: Rank responses to align language models with human feedback without tears
Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang · 2023
Cited alongside, same era.
Removing rlhf protections in gpt-4 via fine-tuning
Qiusi Zhan, Richard Fang, Rohan Bindu, Akul Gupta, Tatsunori Hashimoto, and Daniel Kang · 2023
Cited alongside, same era.
Defending against unforeseen failure modes with latent adversarial training
Tamper-resistant safeguards for open-weight llms
Rishub Tamirisa, Bhrugu Bharathi, Long Phan, Andy Zhou, Alice Gatti, Tarun Suresh, Maxwell Lin, Justin Wang, Rowan Wang, Ron Arel, et al · 2024
Closest in time.
Gemma 2: Improving open language models at a practical size
Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, et al · 2024
Closest in time.
Hˆ 3 fusion: Helpful, harmless, honest fusion of aligned llms
Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu, Zachary Yahn, and Ling Liu · 2024
Closest in time.
Operationalizing a threat model for red-teaming large language models (llms)
Apurv Verma, Satyapriya Krishna, Sebastian Gehrmann, Madhavan Seshadri, Anu Pradhan, Tom Ault, Leslie Barrett, David Rabinowitz, John Doucette, and NhatHai Phan · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Stephen Casper, Lennart Schulze, Oam Patel, and Dylan Hadfield-Menell · 2024
Cited alongside, same era.
Oml: Open, monetizable, and loyal ai
Zerui Cheng, Edoardo Contente, Ben Finch, Oleg Golev, Jonathan Hayase, Andrew Miller, Niusha Moshrefi, Anshul Nasery, Sandeep Nailwal, Sewoong Oh, et al · 2024
Cited alongside, same era.
Safety-aware fine-tuning of large language models
Hyeong Kyu Choi, Xuefeng Du, and Yixuan Li · 2024
Cited alongside, same era.
Recent advances in attack and defense approaches of large language models
Jing Cui, Yishi Xu, Zhewei Huang, Shuchang Zhou, Jianbin Jiao, and Junge Zhang · 2024
Cited alongside, same era.
Towards secure tuning: Mitigating security risks arising from benign instruction fine-tuning
Yanrui Du, Sendong Zhao, Jiawei Cao, Ming Ma, Danyang Zhao, Fenglei Fan, Ting Liu, and Bing Qin · 2024
Cited alongside, same era.
Mimicking user data: On mitigating fine-tuning risks in closed large language models
Francisco Eiras, Aleksandar Petrov, Phillip HS Torr, M Pawan Kumar, and Adel Bibi · 2024
Cited alongside, same era.
Enhancing ai safety through the fusion of low rank adapters
Satya Swaroop Gudipudi, Sreeram Vipparla, Harpreet Singh, Shashwat Goel, and Ponnurangam Kumaraguru · 2024
Cited alongside, same era.
The vllm safety paradox: Dual ease in jailbreak attack and defense
Yangyang Guo, Fangkai Jiao, Liqiang Nie, and Mohan Kankanhalli · 2024
Cited alongside, same era.
Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, and Peter Henderson · 2024
Closest in time.
Di Wu, Xin Lu, Yanyan Zhao, and Bing Qin · 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.
Emerging safety attack and defense in federated instruction tuning of large language models
Rui Ye, Jingyi Chai, Xiangrui Liu, Yaodong Yang, Yanfeng Wang, and Siheng Chen · 2024
Closest in time.
On the vulnerability of safety alignment in open-access llms
Jingwei Yi, Rui Ye, Qisi Chen, Bin Zhu, Siheng Chen, Defu Lian, Guangzhong Sun, Xing Xie, and Fangzhao Wu · 2024
Closest in time.
Ali-agent: Assessing llms’ alignment with human values via agent-based evaluation
Jingnan Zheng, Han Wang, An Zhang, Tai D Nguyen, Jun Sun, and Tat-Seng Chua · 2024
Closest in time.
Locking down the finetuned llms safety
Minjun Zhu, Linyi Yang, Yifan Wei, Ningyu Zhang, and Yue Zhang · 2024
Closest in time.
Safety fine-tuning at (almost) no cost: A baseline for vision large language models
Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, and Timothy Hospedales · 2024
Closest in time.
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
Closest in time.
Open problems in machine unlearning for ai safety
Fazl Barez, Tingchen Fu, Ameya Prabhu, Stephen Casper, Amartya Sanyal, Adel Bibi, Aidan O’Gara, Robert Kirk, Ben Bucknall, Tim Fist, et al · 2025
Closest in time.
On weaponization-resistant large language models with prospect theoretic alignment
Zehua Cheng, Manying Zhang, Jiahao Sun, and Wei Dai · 2025
Closest in time.
Your task may vary: A systematic understanding of alignment and safety degradation when fine-tuning LLMs, 2025
Lei Hsiung, Tianyu Pang, Yung-Chen Tang, Linyue Song, Tsung-Yi Ho, Pin-Yu Chen, and Yaoqing Yang · 2025
Closest in time.
Virus: Harmful fine-tuning attack for large language models bypassing guardrail moderation
Tiansheng Huang, Sihao Hu, Fatih Ilhan, Selim Furkan Tekin, and Ling Liu · 2025
Closest in time.
Safety alignment shouldn’t be complicated, 2025
Jianwei Li and Jung-Eun Kim · 2025
Closest in time.
Salora: Safety-alignment preserved low-rank adaptation
Mingjie Li, Wai Man Si, Michael Backes, Yang Zhang, and Yisen Wang · 2025
Closest in time.
Panacea: Mitigating harmful fine-tuning for large language models via post-fine-tuning perturbation
Yibo Wang, Tiansheng Huang, Li Shen, Huanjin Yao, Haotian Luo, Rui Liu, Naiqiang Tan, Jiaxing Huang, and Dacheng Tao · 2025
Closest in time.
Zhiyuan Xu, Joseph Gardiner, and Sana Belguith · 2025
Closest in time.
Jinluan Yang, Dingnan Jin, Anke Tang, Li Shen, Didi Zhu, Zhengyu Chen, Daixin Wang, Qing Cui, Zhiqiang Zhang, Jun Zhou, et al · 2025
Closest in time.
Probe before you talk: Towards black-box defense against backdoor unalignment for large language models
Biao Yi, Tiansheng Huang, Sishuo Chen, Tong Li, Zheli Liu, Chu Zhixuan, and Yiming Li · 2025
Closest in time.
Identifying and tuning safety neurons in large language models
Yiran Zhao, Wenxuan Zhang, Yuxi Xie, Anirudh Goyal, Kenji Kawaguchi, and Michael Shieh · 2025
Closest in time.