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Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to harmful consequences.
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.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2014
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. 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.
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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Minigpt-v2: Large language model as a unified interface for vision-language multi-task learning
Jun Chen, Deyao Zhu Xiaoqian Shen Xiang Li, Zechun Liu Pengchuan Zhang, Raghuraman Krishnamoorthi Vikas Chandra Yunyang Xiong, and Mohamed Elhoseiny. 2023 · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
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 · 2023
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Holistic analysis of hallucination in gpt-4v (ision): Bias and interference challenges
Chenhang Cui, Yiyang Zhou, Xinyu Yang, Shirley Wu, Linjun Zhang, James Zou, and Huaxiu Yao. 2023 · 2023
Cited alongside, same era.
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 Fung, and Steven Hoi. 2023 · 2023
Cited alongside, same era.
Evaluating object hallucination in large vision-language models
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. 2023 · 2023
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OpenAI. 2023 · 2023
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An early evaluation of gpt-4v (ision)
Yang Wu, Shilong Wang, Hao Yang, Tian Zheng, Hongbo Zhang, Yanyan Zhao, and Bing Qin. 2023 · 2023
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Woodpecker: Hallucination correction for multimodal large language models
Shukang Yin, Chaoyou Fu, Sirui Zhao, Tong Xu, Hao Wang, Dianbo Sui, Yunhang Shen, Ke Li, Xing Sun, and Enhong Chen. 2023 · 2023
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Anisha Gunjal, Jihan Yin, and Erhan Bas. 2023 · 2023
Cited alongside, same era.
Fuxiao Liu, Tianrui Guan, Zongxia Li, Lichang Chen, Yaser Yacoob, Dinesh Manocha, and Tianyi Zhou. 2023a
Cited in the paper.
Aligning large multi-modal model with robust instruction tuning
Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. 2023b
Cited in the paper.
Improved baselines with visual instruction tuning
Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2023c
Cited in the paper.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023d
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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. 2023a
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023b
Cited in the paper.
Evaluation and analysis of hallucination in large vision-language models
Junyang Wang, Yiyang Zhou, Guohai Xu, Pengcheng Shi, Chenlin Zhao, Haiyang Xu, Qinghao Ye, Ming Yan, Ji Zhang, Jihua Zhu, et al. 2023a
Cited in the paper.
Bohan Zhai, Shijia Yang, Xiangchen Zhao, Chenfeng Xu, Sheng Shen, Dongdi Zhao, Kurt Keutzer, Manling Li, Tan Yan, and Xiangjun Fan. 2023 · 2023
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