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The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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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
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Learn to explain: Multimodal reasoning via thought chains for science question answering
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Training language models to follow instructions with human feedback
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Extracting latent steering vectors from pretrained language models
Nishant Subramani, Nivedita Suresh, and Matthew E Peters · 2022
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Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, and Steven Hoi · 2023
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Inspecting and editing knowledge representations in language models
Evan Hernandez, Belinda Z Li, and Jacob Andreas · 2023
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Silkie: Preference distillation for large visual language models
Lei Li, Zhihui Xie, Mukai Li, Shunian Chen, Peiyi Wang, Liang Chen, Yazheng Yang, Benyou Wang, and Lingpeng Kong · 2023
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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
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Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang · 2023
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Activation addition: Steering language models without optimization
Multi-modal hallucination control by visual information grounding
Alessandro Favero, Luca Zancato, Matthew Trager, Siddharth Choudhary, Pramuditha Perera, Alessandro Achille, Ashwin Swaminathan, and Stefano Soatto · 2024
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Eyes closed, safety on: Protecting multimodal llms via image-to-text transformation
Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T Kwok, and Yu Zhang · 2024
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Llavaguard: Vlm-based safeguards for vision dataset curation and safety assessment
Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, and Patrick Schramowski · 2024
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Llava-next: Improved reasoning, ocr, and world knowledge, January 2024a
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Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness
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Alex Turner, Lisa Thiergart, David Udell, Gavin Leech, Ulisse Mini, and Monte MacDiarmid · 2023
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Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization
Zhiyuan Zhao, Bin Wang, Linke Ouyang, Xiaoyi Dong, Jiaqi Wang, and Conghui He · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
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Representation engineering: A top-down approach to ai transparency
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Enhancing large vision language models with self-training on image comprehension
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Sharegpt4v: Improving large multi-modal models with better captions
Lin Chen, Jinsong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin
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Can language models be instructed to protect personal information?
Yang Chen, Ethan Mendes, Sauvik Das, Wei Xu, and Alan Ritter
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Dress: Instructing large vision-language models to align and interact with humans via natural language feedback
Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, and Ajay Divakaran
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On prompt-driven safeguarding for large language models
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Aligning modalities in vision large language models via preference fine-tuning
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Safety fine-tuning at (almost) no cost: A baseline for vision large language models
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