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Existing Large Vision-Language Models (LVLMs) primarily align image features of vision encoder with Large Language Models (LLMs) to leverage their superior text generation capabilities.
Rohrbach, A., Hendricks, L.A., Burns, K., Darrell, T., Saenko, K.: Object hallucination in image captioning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Jan 2018). https://doi.org/10.18653/v1/d18-1437, http://dx.doi.org/10.18653/v1/d18-1437
2018
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North (Jan 2019). https://doi.org/10.18653/v1/n19-1423, http://dx.doi.org/10.18653/v1/n19-1423
2019
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2020
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Amanda, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Tom, H., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Benjamin, C., Clark, J., Berner, C., Sam, M., Radford, A., Sutskever, I., Amodei, D.: Language models are few-shot learners. arXiv: Computation and Language,arXiv: Computation and Language (May 2020)
2020
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Chefer, H., Gur, S., Wolf, L.: Transformer interpretability beyond attention visualization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 782–791 (2021)
2021
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Wei, J., Bosma, M., Zhao, V., Guu, K., Yu, A., Lester, B., Du, N., Dai, A., Le, Q.: Finetuned language models are zero-shot learners. Learning,Learning (Sep 2021)
2021
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Burns, C., Ye, H., Klein, D., Steinhardt, J.: Discovering latent knowledge in language models without supervision (Dec 2022)
2022
Earlier work this paper cites.
Mishra, S., Khashabi, D., Baral, C., Hajishirzi, H.: Cross-task generalization via natural language crowdsourcing instructions. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Jan 2022). https://doi.org/10.18653/v1/2022.acl-long.244, http://dx.doi.org/10.18653/v1/2022.acl-long.244
2022
Earlier work this paper cites.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C.L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback (2022)
2022
Earlier work this paper cites.
Anisha, G., Yin, J., Bas, E.: Detecting and preventing hallucinations in large vision language models (Aug 2023)
2023
Earlier work this paper cites.
Azaria, A., Mitchell, T.: The internal state of an llm knows when its lying (Apr 2023)
2023
Earlier work this paper cites.
Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Muyan, Z., Zhang, Q., Zhu, X., Lu, L., Li, B., Luo, P., Lu, T., Qiao, Y., Dai, J.: Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks (Dec 2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Han, Y., Zhang, C., Chen, X., Yang, X., Wang, Z., Yu, G., Fu, B., Zhang, H.: Chartllama: A multimodal llm for chart understanding and generation (Nov 2023)
2023
Cited alongside, same era.
Huang, Q., Dong, X., Zhang, P., Wang, B., He, C., Wang, J., Lin, D., Zhang, W., Yu, N.: Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation (Nov 2023)
2023
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wei, F., Zhang, X., Zhang, A., Zhang, B., Chu, X.: Lenna: Language enhanced reasoning detection assistant (Dec 2023)
2023
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2023
Cited alongside, same era.
Li, Z., Yang, B., Liu, Q., Ma, Z., Zhang, S., Yang, J., Sun, Y., Liu, Y., Bai, X.: Monkey: Image resolution and text label are important things for large multi-modal models (Nov 2023)
2023
Cited alongside, same era.
Liu, F., Lin, K., Li, L., Wang, J., Yacoob, Y., Wang, L.: Mitigating hallucination in large multi-modal models via robust instruction tuning. In: The Twelfth International Conference on Learning Representations (2023)
2023
Cited alongside, same era.
Liu, F., Lin, K., Li, L., Wang, J., Yacoob, Y., Wang, L.: Mitigating hallucination in large multi-modal models via robust instruction tuning (Sep 2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Sun, Z., Shen, S., Cao, S., Liu, H., Li, C., Shen, Y., Gan, C., Gui, L.Y., Wang, Y.X., Yang, Y., Keutzer, K., Darrell, T.: Aligning large multimodal models with factually augmented rlhf (Sep 2023)
2023
Cited alongside, same era.
Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., Simonyan, K.: Flamingo: a visual language model for few-shot learning
Cited in the paper.
Xiao, G., Tian, Y., Chen, B., Han, S., Lewis, M.: Efficient streaming language models with attention sinks (Sep 2023)
2023
Later among the works it cites.
You, H., Zhang, H., Gan, Z., Du, X., Zhang, B., Wang, Z., Cao, L., Chang, S.F., Yang, Y.: Ferret: Refer and ground anything anywhere at any granularity (Oct 2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhou, Y., Cui, C., Yoon, J., Zhang, L., Deng, Z., Finn, C., Bansal, M., Yao, H.: Analyzing and mitigating object hallucination in large vision-language models (Oct 2023)
2023
Later among the works it cites.
Kai, J., Zhang, T., Hu, H., Lin, Z.: Sh2: Self-highlighted hesitation helps you decode more truthfully (Jan 2024)
2024
Closest in time.
Li, K., Patel, O., Viégas, F., Pfister, H., Wattenberg, M.: Inference-time intervention: Eliciting truthful answers from a language model. Advances in Neural Information Processing Systems 36
2024
Closest in time.