[34] R. Mitchell. Samsung fab data leak: How chatgpt exposed sensitive information. electropages.
[35] OpenAI. Gpt-4 technical report, 2023.
[36] N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar. Semi-supervised knowledge transfer for deep learning from private training data. In International Conference on Learning Representations, 2017.
[37] N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and U. Erlingsson. Scalable private learning with pate. In International Conference on Learning Representations, 2022.
[38] F. Petroni, T. Rocktäschel, P. Lewis, A. Bakhtin, Y. Wu, A. H. Miller, and S. Riedel. Language models as knowledge bases? arXiv preprint arXiv:1909.01066, 2019.
[39] A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, et al. Improving language understanding by generative pre-training. 2018.
[40] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
[41] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485-5551, 2020.
[42] T. L. Scao and A. M. Rush. How many data points is a prompt worth? arXiv preprint arXiv:2103.08493, 2021.
[43] V. Shejwalkar, H. A. Inan, A. Houmansadr, and R. Sim. Membership inference attacks against NLP classification models. In NeurIPS 2021 Workshop Privacy in Machine Learning, 2021.
[44] T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh. Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980, 2020.
[45] R. Shokri, M. Stronati, C. Song, and V. Shmatikov. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3-18. IEEE, 2017.
[46] R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1631-1642, 2013.
[47] C. Song and V. Shmatikov. Auditing data provenance in text-generation models. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 196–206, 2019.
[48] K. Tirumala, A. H. Markosyan, L. Zettlemoyer, and A. Aghajanyan. Memorization without overfitting: Analyzing the training dynamics of large language models. arXiv preprint arXiv:2205.10770, 2022.
[49] F. Tramer and D. Boneh. Differentially private learning needs better features (or much more data). In International Conference on Learning Representations, 2021.
[50] E. M. Voorhees and D. M. Tice. Building a question answering test collection. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pages 200–207, 2000.
[51] A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. R. Bowman. GLUE: A multi-task benchmark and analysis platform for natural language understanding. In International Conference on Learning Representations, 2019.
[52] Z. Wang, W. Hamza, and R. Florian. Bilateral multi-perspective matching for natural language sentences. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 4144-4150, 2017.