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Large Language Models (LLMs) and Vision-Language Models (VLMs) have achieved impressive performance across a wide range of tasks, yet they remain vulnerable to carefully crafted perturbations.
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, and 1 others. 2020 · 1901
Earlier work this paper cites.
Assessment of local influence
R Dennis Cook. 1986 · 1986
Earlier work this paper cites.
Perturbation selection and influence measures in local influence analysis
Hongtu Zhu, Joseph G Ibrahim, Sikyum Lee, and Heping Zhang. 2007 · 2007
Earlier work this paper cites.
Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2009
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2009
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
Earlier work this paper cites.
Sharp minima can generalize for deep nets
Laurent Dinh, Razvan Pascanu, Samy Bengio, and Yoshua Bengio. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr. 2018 · 2018
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Breaking the curse of horizon: Infinite-horizon off-policy estimation
Qiang Liu, Lihong Li, Ziyang Tang, and Dengyong Zhou. 2018 · 2018
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Sensitivity and generalization in neural networks: an empirical study
Roman Novak, Yasaman Bahri, Daniel A Abolafia, Jeffrey Pennington, and Jascha Sohl-Dickstein. 2018 · 2018
Earlier work this paper cites.
Bayesian influence analysis: a geometric approach
Hongtu Zhu, Joseph G Ibrahim, and Niansheng Tang. 2011 · 2020
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
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.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, and 1 others. 2022 · 2022
Cited alongside, same era.
Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022 · 2022
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Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. 2023 · 2023
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Scaling laws for adversarial attacks on language model activations
Stanislav Fort. 2023 · 2023
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Sparsegpt: Massive language models can be accurately pruned in one-shot
Elias Frantar and Dan Alistarh. 2023 · 2023
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and 1 others. 2024 · 2024
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Length-controlled alpacaeval: A simple way to debias automatic evaluators
Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B Hashimoto. 2024 · 2024
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Advancing microbial production through artificial intelligence-aided biology
Xinyu Gong, Jianli Zhang, Qi Gan, Yuxi Teng, Jixin Hou, Yanjun Lyu, Zhengliang Liu, Zihao Wu, Runpeng Dai, Yusong Zou, and 1 others. 2024 · 2024
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The dawn after the dark: An empirical study on factuality hallucination in large language models
Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2024 · 2024
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Cited alongside, same era.
Lord: Low rank decomposition of monolingual code llms for one-shot compression
Ayush Kaushal, Tejas Vaidhya, and Irina Rish. 2023 · 2023
Cited alongside, same era.
Llm-pruner: On the structural pruning of large language models
Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023 · 2023
Cited alongside, same era.
Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models
Erfan Shayegani, Yue Dong, and Nael Abu-Ghazaleh. 2023 · 2023
Cited alongside, same era.
A simple and effective pruning approach for large language models
Mingjie Sun, Zhuang Liu, Anna Bair, and J Zico Kolter. 2023 · 2023
Cited alongside, same era.
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, and 1 others. 2023 · 2023
Cited alongside, same era.
An empirical analysis of parameter-efficient methods for debiasing pre-trained language models
Zhongbin Xie and Thomas Lukasiewicz. 2023 · 2023
Cited alongside, same era.
Slicegpt: Compress large language models by deleting rows and columns
Saleh Ashkboos, Maximilian L Croci, Marcelo Gennari do Nascimento, Torsten Hoefler, and James Hensman. 2024 · 2024
Cited alongside, same era.
Xin Men, Mingyu Xu, Qingyu Zhang, Bingning Wang, Hongyu Lin, Yaojie Lu, Xianpei Han, and Weipeng Chen. 2024 · 2024
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Navigating the safety landscape: Measuring risks in finetuning large language models
Sheng Y Peng, Pin-Yu Chen, Matthew Hull, and Duen H Chau. 2024 · 2024
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Visual adversarial examples jailbreak aligned large language models
Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Peter Henderson, Mengdi Wang, and Prateek Mittal. 2024 · 2024
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Assessing the brittleness of safety alignment via pruning and low-rank modifications
Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, and Peter Henderson. 2024 · 2024
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Ties-merging: Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin A Raffel, and Mohit Bansal. 2024 · 2024
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Language models are super mario: Absorbing abilities from homologous models as a free lunch
Le Yu, Bowen Yu, Haiyang Yu, Fei Huang, and Yongbin Li. 2024 · 2024
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Privacyasst: Safeguarding user privacy in tool-using large language model agents
Xinyu Zhang, Huiyu Xu, Zhongjie Ba, Zhibo Wang, Yuan Hong, Jian Liu, Zhan Qin, and Kui Ren. 2024 · 2024
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Beyond decoder-only: Large language models can be good encoders for machine translation
Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, and 1 others. 2025 · 2025
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An empirical study of the non-determinism of chatgpt in code generation
Shuyin Ouyang, Jie M Zhang, Mark Harman, and Meng Wang. 2025 · 2025
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