1. R. Aljundi, L. Caccia, E. Belilovsky, M. Caccia, M. Lin, L. Charlin, T. Tuytelaars, Online continual learning with maximally interfered retrieval, in Advances in Neural Information Processing Systems (2019)
2. S. Bao, H. He, F. Wang, W. Hua, H. Wang, W. Wenquan, W. Zhihua, Z. Guo, L. Hua, X. Huang, et al., Plato-xl: Exploring the large-scale pre-training of dialogue generation, in Findings of the Association for Computational Linguistics: AACL-IJCNLP (2022), pp. 107–118
3. E.M. Bender, T. Gebru, A. McMillan-Major, S. Shmitchell, On the dangers of stochastic parrots: can language models be too big? in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (2021), pp. 610–623
4. R. Bommasani, D.A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M.S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al., On the opportunities and risks of foundation models (2021). arXiv:2108.07258
5. Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazare, and Jason Weston. Learning from dialogue after deployment: Feed yourself, chatbot! In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (2019), pp. 3667–3684