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Multimodal Large Language Models (MLLMs) are known to hallucinate, which limits their practical applications.
Object hallucination in image captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Earlier work this paper cites.
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
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Sharegpt4v: Improving large multi-modal models with better captions
Lin Chen, Jisong Li, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao, and Dahua Lin. 2023 · 2023
Earlier work this paper cites.
Evaluating object hallucination in large vision-language models
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Xin Zhao, and Ji-Rong Wen. 2023 · 2023
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An llm-free multi-dimensional benchmark for mllms hallucination evaluation
Junyang Wang, Yuhang Wang, Guohai Xu, Jing Zhang, Yukai Gu, Haitao Jia, Ming Yan, Ji Zhang, and Jitao Sang. 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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Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization
Zhiyuan Zhao, Bin Wang, Linke Ouyang, Xiaoyi Dong, Jiaqi Wang, and Conghui He. 2023 · 2023
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Hallucination of multimodal large language models: A survey
Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He, Zongbo Han, Zheng Zhang, and Mike Zheng Shou. 2024 · 2024
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HALC: Object hallucination reduction via adaptive focal-contrast decoding
Zhaorun Chen, Zhuokai Zhao, Hongyin Luo, Huaxiu Yao, Bo Li, and Jiawei Zhou. 2024 · 2024
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A survey on multimodal large language models for autonomous driving
Can Cui, Yunsheng Ma, Xu Cao, Wenqian Ye, Yang Zhou, Kaizhao Liang, Jintai Chen, Juanwu Lu, Zichong Yang, Kuei-Da Liao, et al. 2024 · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
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Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation
Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, and Nenghai Yu. 2024 · 2024
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Self-introspective decoding: Alleviating hallucinations for large vision-language models
Fushuo Huo, Wenchao Xu, Zhong Zhang, Haozhao Wang, Zhicheng Chen, and Peilin Zhao. 2024 · 2024
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Volcano: Mitigating multimodal hallucination through self-feedback guided revision
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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Hunyuan-large: An open-source moe model with 52 billion activated parameters by tencent
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Eyes wide shut? exploring the visual shortcomings of multimodal llms
Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, and Saining Xie. 2024 · 2024
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Logical closed loop: Uncovering object hallucinations in large vision-language models
Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang, Shu Wu, Liang Wang, and Tieniu Tan. 2024 · 2024
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Less is more: Mitigating multimodal hallucination from an EOS decision perspective
Zihao Yue, Liang Zhang, and Qin Jin. 2024 · 2024
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Seongyun Lee, Sue Hyun Park, Yongrae Jo, and Minjoon Seo. 2024 · 2024
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Mitigating object hallucinations in large vision-language models through visual contrastive decoding
Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, and Lidong Bing. 2024 · 2024
Cited alongside, same era.
VLFeedback: A large-scale AI feedback dataset for large vision-language models alignment
Lei Li, Zhihui Xie, Mukai Li, Shunian Chen, Peiyi Wang, Liang Chen, Yazheng Yang, Benyou Wang, Lingpeng Kong, and Qi Liu. 2024 · 2024
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Hello GPT-4o
OpenAI. 2024 · 2024
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Clip-dpo: Vision-language models as a source of preference for fixing hallucinations in lvlms
Yassine Ouali, Adrian Bulat, Brais Martinez, and Georgios Tzimiropoulos. 2025 · 2024
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A medical multimodal large language model for future pandemics
Fenglin Liu, Tingting Zhu, Xian Wu, Bang Yang, Chenyu You, Chenyang Wang, Lei Lu, Zhangdaihong Liu, Yefeng Zheng, Xu Sun, et al. 2023a
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Mitigating hallucination in large multi-modal models via robust instruction tuning
Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, and Lijuan Wang. 2023b
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Visual instruction tuning
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024a
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Pip: Detecting adversarial examples in large vision-language models via attention patterns of irrelevant probe questions
Yudong Zhang, Ruobing Xie, Jiansheng Chen, Xingwu Sun, and Yu Wang. 2024 · 2024
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Self-supervised visual preference alignment
Ke Zhu, Liang Zhao, Zheng Ge, and Xiangyu Zhang. 2024 · 2024
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Strengthening multimodal large language model with bootstrapped preference optimization
Renjie Pi, Tianyang Han, Wei Xiong, Jipeng Zhang, Runtao Liu, Rui Pan, and Tong Zhang. 2025 · 2025
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