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Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E Terry · 1952
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The identification of nonlinear discrete-time fading-memory systems using neural network models
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A diversity-promoting objective function for neural conversation models
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The factual inconsistency problem in abstractive text summarization: A survey
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Learning transferable visual models from natural language supervision
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Training language models to follow instructions with human feedback
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Chain-of-thought prompting elicits reasoning in large language models
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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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Trusting your evidence: Hallucinate less with context-aware decoding
Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Scott Wen-tau Yih · 2023
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Aligning large multimodal models with factually augmented rlhf
Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Gui, Yu-Xiong Wang, Yiming Yang, et al · 2023
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Locally typical sampling
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Contrastive decoding: Open-ended text generation as optimization, 2023a
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Evaluating object hallucination in large vision-language models
Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen
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Aligning large multi-modal model with robust instruction tuning
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Evaluation and analysis of hallucination in large vision-language models
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mplug-owl: Modularization empowers large language models with multimodality
Qinghao Ye, Haiyang Xu, Guohai Xu, Jiabo Ye, Ming Yan, Yiyang Zhou, Junyang Wang, Anwen Hu, Pengcheng Shi, Yaya Shi, et al · 2023
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Analyzing and mitigating object hallucination in large vision-language models
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
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