1. Beltagy, I., Lo, K., Cohan, A.: Scibert: a pretrained language model for scientific text. In: Proceedings of EMNLP-IJCNLP 2019. ACL (2019). https://doi.org/10.18653/v1/D19-1371
2. Bottomley, J.: Academic Writing for International Students of Science. Routledge, Abingdon (2022)
3. Cao, Y., Wan, X.: DivGAN: towards diverse paraphrase generation via diversified generative adversarial network. In: Findings of the Association for Computational Linguistics: EMNLP 2020. ACL (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.218
4. Carlini, N., et al.: Extracting training data from large language models. In: Bailey, M., Greenstadt, R. (eds.) 30th USENIX Security Symposium, USENIX Security 2021(August), pp. 11–13, 2021. pp. 2633–2650. USENIX Association (2021). https://www.usenix.org/conference/usenixsecurity21/presentation/carlini-extracting
5. Clark, K., Luong, M., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. In: ICLR 2020. OpenReview.net (2020)