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Instruction-following Vision Large Language Models (VLLMs) have achieved significant progress recently on a variety of tasks.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Bradley, R. A. and Terry, M. E · 1952
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Vqa: Visual question answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., and Parikh, D · 2015
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Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D · 2015
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Proximal policy optimization algorithms, 2017
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Object hallucination in image captioning
Rohrbach, A., Hendricks, L. A., Burns, K., Darrell, T., and Saenko, K · 2018
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q., Sung, Y.-H., Li, Z., and Duerig, T · 2021
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., et al · 2022
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Li, J., Li, D., Xiong, C., and Hoi, S · 2022
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Learn to explain: Multimodal reasoning via thought chains for science question answering
Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.-W., Zhu, S.-C., Tafjord, O., Clark, P., and Kalyan, A · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
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Qwen-vl: A frontier large vision-language model with versatile abilities
Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., and Zhou, J · 2023
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Honeybee: Locality-enhanced projector for multimodal llm
Cha, J., Kang, W., Mun, J., and Roh, B · 2023
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Lion: Empowering multimodal large language model with dual-level visual knowledge
Chen, G., Shen, L., Shao, R., Deng, X., and Nie, L · 2023
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Debiasing vision-language models via biased prompts
Chuang, C.-Y., Jampani, V., Li, Y., Torralba, A., and Jegelka, S · 2023
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Holistic analysis of hallucination in gpt-4v (ision): Bias and interference challenges
Cui, C., Zhou, Y., Yang, X., Wu, S., Zhang, L., Zou, J., and Yao, H · 2023
Cited alongside, same era.
Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
Mitigating object hallucinations in large vision-language models through visual contrastive decoding
Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., and Bing, L · 2023
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OpenAI · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2023
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Aligning large multimodal models with factually augmented rlhf
Sun, Z., Shen, S., Cao, S., Liu, H., Li, C., Shen, Y., Gan, C., Gui, L.-Y., Wang, Y.-X., Yang, Y., et al · 2023
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Stanford alpaca: An instruction-following llama model
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Dai, W., Li, J., Li, D., Tiong, A. M. H., Zhao, J., Wang, W., Li, B., Fung, P., and Hoi, S · 2023
Cited alongside, same era.
Talk2bev: Language-enhanced bird’s-eye view maps for autonomous driving
Dewangan, V., Choudhary, T., Chandhok, S., Priyadarshan, S., Jain, A., Singh, A. K., Srivastava, S., Jatavallabhula, K. M., and Krishna, K. M · 2023
Cited alongside, same era.
Mme: A comprehensive evaluation benchmark for multimodal large language models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., et al · 2023
Cited alongside, same era.
Scaling laws for reward model overoptimization
Gao, L., Schulman, J., and Hilton, J · 2023
Cited alongside, same era.
Huang, Q., Dong, X., Zhang, P., Wang, B., He, C., Wang, J., Lin, D., Zhang, W., and Yu, N · 2023
Cited alongside, same era.
Rlaif: Scaling reinforcement learning from human feedback with ai feedback
Lee, H., Phatale, S., Mansoor, H., Lu, K., Mesnard, T., Bishop, C., Carbune, V., and Rastogi, A · 2023
Cited alongside, same era.
Vision-language instruction tuning: A review and analysis
Li, C., Ge, Y., Li, D., and Shan, Y
Cited in the paper.
Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Li, C., Wong, C., Zhang, S., Usuyama, N., Liu, H., Yang, J., Naumann, T., Poon, H., and Gao, J
Cited in the paper.
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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How many unicorns are in this image? a safety evaluation benchmark for vision llms
Tu, H., Cui, C., Wang, Z., Zhou, Y., Zhao, B., Han, J., Zhou, W., Yao, H., and Xie, C · 2023
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mplug-owl: Modularization empowers large language models with multimodality
Ye, Q., Xu, H., Xu, G., Ye, J., Yan, M., Zhou, Y., Wang, J., Hu, A., Shi, P., Shi, Y., et al · 2023
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Woodpecker: Hallucination correction for multimodal large language models
Yin, S., Fu, C., Zhao, S., Xu, T., Wang, H., Sui, D., Shen, Y., Li, K., Sun, X., and Chen, E · 2023
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Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization
Zhao, Z., Wang, B., Ouyang, L., Dong, X., Wang, J., and He, C · 2023
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Analyzing and mitigating object hallucination in large vision-language models
Zhou, Y., Cui, C., Yoon, J., Zhang, L., Deng, Z., Finn, C., Bansal, M., and Yao, H · 2023
Later among the works it cites.