[52] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015.
[53] K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu. Mpnet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, 33:16857-16867, 2020.
[54] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
[55] N. Stiennon, L. Ouyang, J. Wu, D. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008-3021, 2020.
[56] H. Su, J. Kasai, C. H. Wu, W. Shi, T. Wang, J. Xin, R. Zhang, M. Ostendorf, L. Zettlemoyer, N. A. Smith, et al. Selective annotation makes language models better few-shot learners. arXiv preprint arXiv:2209.01975, 2022.
[57] L. van der Maaten and G. Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(86):2579-2605, 2008.
[58] Z. J. Wang, E. Montoya, D. Munechika, H. Yang, B. Hoover, and D. H. Chau. DiffusionDB: A large-scale prompt gallery dataset for text-to-image generative models. arXiv:2210.14896 [cs], 2022.
[59] G. Warnell, N. Waytowich, V. Lawhern, and P. Stone. Deep tamer: Interactive agent shaping in high-dimensional state spaces. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
[60] J. Wu, L. Ouyang, D. Ziegler, N. Stiannon, R. Lowe, J. Leike, and P. Christiano. Recursively summarizing books with human feedback, Sep 2021.
[61] X. Wu, K. Sun, F. Zhu, R. Zhao, and H. Li. Better aligning text-to-image models with human preference. arXiv preprint arXiv:2303.14420, 2023.
[62] X. Xu, Z. Wang, E. Zhang, K. Wang, and H. Shi. Versatile diffusion: Text, images and variations all in one diffusion model. arXiv preprint arXiv:2211.08332, 2022.
[63] J. Yu, Y. Xu, J. Y. Koh, T. Luong, G. Baid, Z. Wang, V. Vasudevan, A. Ku, Y. Yang, B. K. Ayan, et al. Scaling autoregressive models for content-rich text-to-image generation. Transactions on Machine Learning Research, 2022.
[64] A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia, et al. Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414, 2022.
[65] J. Zhang, Y. Liu, J. Mao, W. Ma, J. Xu, S. Ma, and Q. Tian. User behavior simulation for search result re-ranking. ACM Transactions on Information Systems, 41(1):1-35, 2023.
[66] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068, 2022.
[67] W. Zhou and K. Xu. Learning to compare for better training and evaluation of open domain natural language generation models. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05):9717-9724, Jun 2020.
[68] D. Ziegler, N. Stiennon, J. Wu, T. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving. Fine-tuning language models from human preferences., Sep 2019.