Fetching the paper…
Reading the bibliography…
Large vision-language models (LVLMs), designed to interpret and respond to human instructions, occasionally generate hallucinated or harmful content due to inappropriate instructions.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: ImageNet: large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
Gurari, D., Li, Q., Stangl, A.J., Guo, A., Lin, C., Grauman, K., Luo, J., Bigham, J.P.: VizWiz grand challenge: Answering visual questions from blind people. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3608–3617 (2018)
2018
Earlier work this paper cites.
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748–8763 (2021)
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
Dai, W., Li, J., Li, D., Tiong, A.M.H., Zhao, J., Wang, W., Li, B., Fung, P., Hoi, S.: InstructBLIP: Towards general-purpose vision-language models with instruction tuning (2023)
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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.
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Slobodkin, A., Goldman, O., Caciularu, A., Dagan, I., Ravfogel, S.: The curious case of hallucinatory (un)answerability: Finding truths in the hidden states of over-confident large language models. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 3607–3625 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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.
Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in Neural Information Processing Systems 36
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.