1. Brown T., Mann B., Ryder N., Subbiah M., Kaplan J. D., …, Amodei D. Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020, vol. 33, рр. 1877–1901.
2. Radford A., Wu J., Child R., Luan D., Amodei D., Sutskever I. Language models are unsupervised multitask learners. OpenAI, 2019. Available at: https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (accessed 03.11.2025).
3. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., …, Polosukhin I. Attention is all you need. Advances in Neural Information Processing Systems, 2017, vol. 30, рр. 5998–6008.
4. Artetxe M., Ruder S., Yogatama D. On the cross-lingual transferability of monolingual representations. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5–10 July 2020, рр. 4623–4637.
5. Jakubíček M., Kilgarriff A., Kovář V., Rychlỳ P., Suchomel V. The tenten corpus family. Proceedings of the 7th International Corpus Linguistics Conference (CL2013), Lancaster University, United Kingdom, 22–26 July 2013, рр. 125–127.
6. Sennrich R., Haddow B., Birch A. Neural machine translation of rare words with subword units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 7–12 August 2016, vol. 1, рр. 1715–1725.
7. Imamura K., Sumita E. Vocabulary adaptation for domain adaptation in neural machine translation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 31 October – 4 November 2018, рр. 4623–4637.
8. Ghazvininejad M., Shi X., Choi Y., Knight K. Hafez: an interactive poetry generation system. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Canada, 30 July – 4 August 2017, рр. 43–48.
9. Mesnard T., Hardin C., Dadashi R., Bhupatiraju S., Pathak S., …, Eck D. Gemma: Open models based on Gemini research and technology, 2024. Available at: https://arxiv.org/pdf/2403.08295 (accessed 03.11.2025).
10. Lau J. H., Cohn T., Baldwin T., Brooke J., Hammond A. Deep-speare: A joint neural model of poetic language, meter and rhyme. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 15–20 July 2018, vol. 1, рр. 1948–1958.
11. Zugarini A., Melacci S., Maggini M. Neural poetry: Learning to generate poems using syllables. Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, 17–19 September 2019, рр. 313–325.
