1. Agrawal, S., Zhou, C., Lewis, M., Zettlemoyer, L., Ghazvininejad, M.: In-context examples selection for machine translation. In: Findings of the Association for Computational Linguistics: ACL 2023, pp. 8857–8873. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.findings-acl.564
2. Bawden, R., Poinhos, J., Kogkitsidou, E., Gambette, P., Sagot, B., Gabay, S.: Automatic normalisation of early Modern French. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 3354–3366. European Language Resources Association, Marseille, France (2022). https://aclanthology.org/2022.lrec-1.358/
3. Chrabrowa, A., et al.: Evaluation of transfer learning for Polish with a text-to-text model. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 4374–4394. European Language Resources Association, Marseille, France (2022). https://aclanthology.org/2022.lrec-1.466
4. Ding, B., et al.: Is GPT-3 a good data annotator? In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 11173–11195. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-long.626
5. Gilardi, F., Alizadeh, M., Kubli, M.: ChatGPT outperforms crowd-workers for text-annotation tasks. arXiv:2303.15056 (2023)