Fetching the paper…
Reading the bibliography…
Large vision-language models (LVLMs) are prone to hallucinations, where certain contextual cues in an image can trigger the language module to produce overconfident and incorrect reasoning about abnormal or hypothetical objects.
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, et al. 2020 · 1901
Earlier work this paper cites.
The theory of cognitive dissonance: A current perspective
Elliot Aronson. 1969 · 1969
Earlier work this paper cites.
Nonverbal expectancy violations: Model elaboration and application to immediacy behaviors
Judee K Burgoon and Jerold L Hale. 1988 · 1988
Earlier work this paper cites.
Interpersonal expectations, expectancy violations, and emotional communication
Judee K Burgoon. 1993 · 1993
Earlier work this paper cites.
Culture and cognition
Paul DiMaggio. 1997 · 1997
Earlier work this paper cites.
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 · 2014
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. 2016 · 2016
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017 · 2017
Earlier work this paper cites.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. 2017 · 2017
Earlier work this paper cites.
Schemata: The building blocks of cognition
David E Rumelhart. 2017 · 2017
Earlier work this paper cites.
Object hallucination in image captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Earlier work this paper cites.
An introduction to cognitive dissonance theory and an overview of current perspectives on the theory
Eddie Harmon-Jones and Judson Mills. 2019 · 2019
Earlier work this paper cites.
Cultural schemas: What they are, how to find them, and what to do once you’ve caught one
Andrei Boutyline and Laura K Soter. 2021 · 2021
Earlier work this paper cites.
Background remover: Remove background from images and video using ai
Johnathan Nader. 2021 · 2021
Earlier work this paper cites.
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 · 2021
Earlier work this paper cites.
Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. 2022 · 2022
Earlier work this paper cites.
Let there be a clock on the beach: Reducing object hallucination in image captioning
Ali Furkan Biten, Lluís Gómez, and Dimosthenis Karatzas. 2022 · 2022
Earlier work this paper cites.
Simple open-vocabulary object detection with vision transformers
M Minderer, A Gritsenko, A Stone, M Neumann, D Weissenborn, A Dosovitskiy, A Mahendran, A Arnab, M Dehghani, Z Shen, et al. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Chatcad: Interactive computer-aided diagnosis on medical image using large language models
Sheng Wang, Zihao Zhao, Xi Ouyang, Qian Wang, and Dinggang Shen. 2023 · 2023
Later among the works it cites.
Wizardlm: Empowering large language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023 · 2023
Later among the works it cites.
The dawn of lmms: Preliminary explorations with gpt-4v (ision)
Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. 2023 · 2023
Later among the works it cites.
Halle-switch: Controlling object hallucination in large vision language models
Bohan Zhai, Shijia Yang, Chenfeng Xu, Sheng Shen, Kurt Keutzer, and Manling Li. 2023 · 2023
Later among the works it cites.
Siren’s song in the ai ocean: A survey on hallucination in large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Assaf Ben-Kish, Moran Yanuka, Morris Alper, Raja Giryes, and Hadar Averbuch-Elor. 2023 · 2023
Cited alongside, same era.
Evaluating a large language model on searching for gui layouts
Paul Brie, Nicolas Burny, Arthur Sluÿters, and Jean Vanderdonckt. 2023 · 2023
Cited alongside, same era.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al. 2023 · 2023
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023 · 2023
Cited alongside, same era.
Detecting and preventing hallucinations in large vision language models
Anish Gunjal, Jihan Yin, and Erhan Bas. 2023 · 2023
Cited alongside, same era.
Advancing medical imaging with language models: A journey from n-grams to chatgpt
Mingzhe Hu, Shaoyan Pan, Yuheng Li, and Xiaofeng Yang. 2023 · 2023
Cited alongside, same era.
Exposing and mitigating spurious correlations for cross-modal retrieval
Jae Myung Kim, A Koepke, Cordelia Schmid, and Zeynep Akata. 2023 · 2023
Cited alongside, same era.
Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2023 · 2023
Cited alongside, same era.
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. 2023 · 2023
Later among the works it cites.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
Later among the works it cites.
Bard: Google ai’s conversational ai
Google AI. 2023 · 2024
Closest in time.
Driving with llms: Fusing object-level vector modality for explainable autonomous driving
Long Chen, Oleg Sinavski, Jan Hünermann, Alice Karnsund, Andrew James Willmott, Danny Birch, Daniel Maund, and Jamie Shotton. 2024 · 2024
Closest in time.
Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi. 2024 · 2024
Closest in time.
The instinctive bias: Spurious images lead to hallucination in mllms
Tianyang Han, Qing Lian, Rui Pan, Renjie Pi, Jipeng Zhang, Shizhe Diao, Yong Lin, and Tong Zhang. 2024 · 2024
Closest in time.
Chaoya Jiang, Wei Ye, Mengfan Dong, Hongrui Jia, Haiyang Xu, Ming Yan, Ji Zhang, and Shikun Zhang. 2024 · 2024
Closest in time.
Llm-grounded video diffusion models
Long Lian, Baifeng Shi, Adam Yala, Trevor Darrell, and Boyi Li. 2024 · 2024
Closest in time.
How easy is it to fool your multimodal llms? an empirical analysis on deceptive prompts
Yusu Qian, Haotian Zhang, Yinfei Yang, and Zhe Gan. 2024 · 2024
Closest in time.
Claude 3
Anthropic Team. 2024 · 2024
Closest in time.
Haloquest: A visual hallucination dataset for advancing multimodal reasoning
Zhecan Wang, Garrett Bingham, Adams Yu, Quoc Le, Thang Luong, and Golnaz Ghiasi. 2024 · 2024
Closest in time.
Xiyang Wu, Ruiqi Xian, Tianrui Guan, Jing Liang, Souradip Chakraborty, Fuxiao Liu, Brian Sadler, Dinesh Manocha, and Amrit Singh Bedi. 2024 · 2024
Closest in time.