Fetching the paper…
Reading the bibliography…
Autoregressive models, such as the GPT family, use a fixed order, usually left-to-right, to generate sequences.
Merity, S., Xiong, C., Bradbury, J., Socher, R.: Pointer sentinel mixture models. In: International Conference on Learning Representations (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017), https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
Earlier work this paper cites.
Gokaslan, A., Cohen, V.: Openwebtext corpus. http://Skylion007.github.io/OpenWebTextCorpus (2019)
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
Earlier work this paper cites.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: Generalized autoregressive pretraining for language understanding. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019)
2019
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Austin, J., Johnson, D.D., Ho, J., Tarlow, D., Van Den Berg, R.: Structured denoising diffusion models in discrete state-spaces. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.
Chang, H., Zhang, H., Jiang, L., Liu, C., Freeman, W.T.: Maskgit: Masked generative image transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11315–11325 (June 2022)
2022
Earlier work this paper cites.
Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022)
2022
Cited alongside, same era.
Lee, D., Kim, C., Kim, S., Cho, M., HAN, W.S.: Draft-and-revise: Effective image generation with contextual rq-transformer. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems. vol. 35, pp. 30127–30138. Curran Associates, Inc. (2022)
2022
Cited alongside, same era.
Lezama, J., Chang, H., Jiang, L., Essa, I.: Improved masked image generation with token-critic. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022. pp. 70–86. Springer Nature Switzerland, Cham (2022)
2022
Cited alongside, same era.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., brian ichter, Xia, F., Chi, E.H., Le, Q.V., Zhou, D.: Chain of thought prompting elicits reasoning in large language models. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022)
2023
Later among the works it cites.
Villegas, R., Babaeizadeh, M., Kindermans, P.J., Moraldo, H., Zhang, H., Saffar, M.T., Castro, S., Kunze, J., Erhan, D.: Phenaki: Variable length video generation from open domain textual descriptions. In: International Conference on Learning Representations (2023)
2023
Later among the works it cites.
Golovneva, O., Allen-Zhu, Z., Weston, J., Sukhbaatar, S.: Reverse training to nurse the reversal curse (2024)
2024
Closest in time.
Kakogeorgiou, I., Gidaris, S., Karantzalos, K., Komodakis, N.: Spot: Self-training with patch-order permutation for object-centric learning with autoregressive transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 22776–22786 (June 2024)
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
Chang, H., Zhang, H., Barber, J., Maschinot, A., Lezama, J., Jiang, L., Yang, M.H., Murphy, K., Freeman, W.T., Rubinstein, M., Li, Y., Krishnan, D.: Muse: Text-to-image generation via masked generative transformers (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Gulrajani, I., Hashimoto, T.: Likelihood-based diffusion language models. In: Thirty-seventh Conference on Neural Information Processing Systems (2023), https://openreview.net/forum?id=e2MCL6hObn
2023
Cited alongside, same era.
Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models. In: Proceedings of the 40th International Conference on Machine Learning. ICML’23, JMLR.org (2023)
2023
Cited alongside, same era.
Lou, A., Meng, C., Ermon, S.: Discrete diffusion modeling by estimating the ratios of the data distribution (2024)
2024
Closest in time.
OpenAI: New embedding models and api updates. https://openai.com/blog/new-embedding-models-and-api-updates (2024), accessed: [01.03.2024]
2024
Closest in time.
Papadopoulos, V., Wenger, J., Hongler, C.: Arrows of time for large language models (2024)
2024
Closest in time.